关于PID控制的毕业设计外文翻译

PID中英文对照翻译

中英文互译PID Contro lIntroductionThe PID controller is the most common form of feedback 。

It was an essential element of early governors and it became the standard tool when process control emerged in the 1940s. In process control today, more than 95％ of the control loops are of PID type, most loops are actually PI control. PID controllers are today found in all areas where control is used 。

The controllers come in many different forms. There are standalone systems in boxes for one or a few loops, which are manufactured by the hundred thousands yearly 。

PID control is an important ingredient of a distributed control system 。

The controllers are also embedded in many special purpose control systems 。

PID control is often combined with logic ， sequential functions, selectors ， and simple function blocks to build the complicated automation systems used for energy production, transportation, and manufacturing. Many sophisticated control strategies ， such as model predictive control, are also organized hierarchically. PID control is used at the lowest level; the multivariable controller gives the set points to the controllers at the lower level. The PID controller can thus be said to be the “bread and butter of control engineering。

PID控制中英文对照翻译

外文资料与翻译PID Contro lIntroductionThe PID controller is the most common form of feedback. It was an essential element of early governors and it became the standard tool when process control emerged in the 1940s. In process control today, more than 95% of the control loops are of PID type, most loops are actually PI control. PID controllers are today found in all areas where control is used. The controllers come in many different forms. There are standalone systems in boxes for one or a few loops, which are manufactured by the hundred thousands yearly. PID control is an important ingredient of a distributed control system. The controllers are also embedded in many special purpose control systems. PID control is often combined with logic, sequential functions, selectors, and simple function blocks to build the complicated automation systems used for energy production, transportation, and manufacturing. Many sophisticated control strategies, such as model predictive control, are also organized hierarchically. PID control is used at the lowest level; the multivariable controller gives the set points to the controllers at the lower level. The PID controller can thus be said to be the “bread and butter of control engineering. It is an important component in every co ntrol engineer’s tool box.PID controllers have survived many changes in technology, from mechanics and pneumatics to microprocessors via electronic tubes, transistors, integrated circuits. The microprocessor has had a dramatic influence the PID controller. Practically all PID controllers made today are based on microprocessors. This has given opportunities to provide additional features like automatic tuning, gain scheduling, and continuous adaptation.6.2 The AlgorithmWe will start by summarizing the key features of the PID controller. The “textbook” version of the PID algorithm is described by:()()()()⎪⎪⎭⎫ ⎝⎛++=⎰dt t de d e t e K t u T T d t i 01ττ 6.1 where y is the measured process variable, r the reference variable, u is the control signal and e is the control error （e =sp y − y ）. The reference variable is often calledthe set point. The control signal is thus a sum of three terms: the P-term （which is proportional to the error）, the I-term （which is proportional to the integral of the error）, and the D-term （which is proportional to the derivative of the error）. The controller parameters are proportional gain K, integral time T i, and derivative time T d. The integral, proportional and derivative part can be interpreted as control actions based on the past, the present and the future as is illustrated in Figure 2.2. The derivative part can also be interpreted as prediction by linear extrapolation as is illustrated in Figure 2.2. The action of the different terms can be illustrated by the following figures which show the response to step changes in the reference value in a typical case.Effects of Proportional, Integral and Derivative ActionProportional control is illustrated in Figure 6.1. The controller is given by D6.1E with T i= and T d=0. The figure shows that there is always a steady state error in proportional control. The error will decrease with increasing gain, but the tendency towards oscillation will also increase.Figure 6.2 illustrates the effects of adding integral. It follows from D6.1E that the strength of integral action increases with decreasing integral time T i. The figure shows that the steady state error disappears when integral action is used. Compare with the discussion of the “magic of integral action” in Sec tion 2.2. The tendency for oscillation also increases with decreasing T i. The properties of derivative action are illustrated in Figure 6.3.Figure 6.3 illustrates the effects of adding derivative action. The parameters K and T i are chosen so that the closed loop system is oscillatory. Damping increases with increasing derivative time, but decreases again when derivative time becomes too large. Recall that derivative action can be interpreted as providing prediction by linear extrapolation over the time T d. Using this interpretation it is easy to understand that derivative action does not help if the prediction time T d is too large. In Figure 6.3 the period of oscillation is about 6 s for the system without derivative Chapter 6. PID ControlFigure 6.1Figure 6.2Derivative actions cease to be effective when T d is larger than a 1 s (one sixth of the period). Also notice that the period of oscillation increases when derivative time is increased.A PerspectiveThere is much more to PID than is revealed by （6.1）. A faithful implementation of the equation will actually not result in a good controller. To obtain a good PID controller it is also necessary to consider。

PID控制简介中英文翻译word版

英语资料及译文About PID controlRecently automation technology is based on the concept of feedback. Elements of feedback theory consist of three parts: measurement, comparison and implementation. Measurement variables of concern, compared with expectations, with the control system to correct the error response.The theory and application of automatic control, the key is to make the correct measurement and comparison of how best to rectify the system.PID (proportional - integral - differential) controller as the first practical use of the controller more than 50 years of history, is still the most widely used industrial controller. Simple PID controller, the use of the system does not accurately model a prerequisite that they have become the most widely used controller.PID controller is the proportion of cells (P), integral unit (I) and the differential unit (D) component.Because of its wide range of uses, the use of flexible, has been serialized products, the use of only three parameters setting (Kp, Ti and Td) can be. In many cases, does not necessarily need all three modules, which can take 1-2 unit, but the proportion of the control unit is essential.First of all, PID broad range of applications. Although many industrial processes or time-varying non-linear, but can be simplified through their basic non-linear and dynamic characteristics of the system over time, so that you can control the PID.Secondly, PID parameter can tune easier. That is, PID parameters Kp, Ti and Td can be based on the dynamic characteristics of the process of setting a timely manner. If the dynamic characteristics of the process ofchange, for example, changes may be caused by the load dynamic characteristics of the system changes, PID parameters can be re-tuning.Third, PID controller in practice is to be improved continuously, the following are two examples of improvements.In factories, we always see a lot of loops are in manual, and because of the difficulty of the course so that the "automatic" mode, a smooth working. As a result of these deficiencies, the use of the industrial control system PID is always subject to product quality, safety, waste production and energy problems. PID parameter self-tuning PID parameters in order to deal with this problem setting generated. Now, the auto-tuning or self-tuning of PID controller is a business single-loop controllers and distributed control system of a standard.In some cases the system-specific design of PID controller to control very well, but they are there are still some problems to be solved: If self-tuning should be based on the model, in order to re-PID tuning parameters online to find and maintain a good process model is more difficult. When closed-loop works, the requirements in the process of inserting must have a test signal. This method will cause disturbance, so model-based PID parameter self-tuning is not too good in the industrial applications.If self-tuning control law based on the often difficult to load disturbance caused by the impact and dynamic characteristics of the process of the impact of changes in the distinction between the effects of so disturbed overshoot controller will have to create a self-adaptive unnecessary conversion. In addition, since the control law based on the maturity of the system is not the stability of analytical methods, the reliability of parameter tuning, there are many problems.Therefore, many self-tuning PID controller parameters work in the auto-tuning mode and not in the self-tuning mode. Auto-tuning is oftenused to describe the state of open-loop based on a simple process model to determine automatic calculation of PID parameters.PID in controlling nonlinear, time-varying, coupling and parameter uncertainty and structural complexity of the process, the work is not very good. The most important thing is, if the PID controller can not control the complexity of the process, regardless of how not to use transfer parameters.Despite these shortcomings, PID controller is sometimes the most simple is the best controller.At present, the level of industrial automation has become a measure of the level of modernization in all walks of life an important sign. At the same time, the development of control theory has also experienced a classical control theory, modern control theory and intelligent control theory of three stages. Classic example of intelligent control is ambiguous, such as full-automatic washing machine. Automatic control system can be divided into open-loop control systems and closed-loop control system.A control system, including controllers, sensors, transmitters, implementing agencies, input and output interfaces. Controller's output after the output interface, the implementing agencies, added to the system was charged with; control system charged with the amount, after the sensor, transmitter, through the input interface to the controller. Different control system, the sensor, transmitter, the executing agency is not the same. For example pressure sensors need to be used in pressure control system. Electric heating control system is the sensor temperature sensor. At present, PID control and PID controller or a smart controller (instrument) has a lot of products have been in practice in engineering is widely used, there are a wide range of PID controller products, major companies have developed with PID parameter self-tuning regulator function smart (intelligent regulator), which the PID controller parameters are automatically adjusted through the intelligent or self-tuning, adaptivealgorithms to achieve. PID control are achieved using pressure, temperature, flow, liquid level controller, PID control functions to achieve the programmable logic controller (PLC), also enables the PC system, PID control and so on. Programmable Logic Controller (PLC) is the use of its closed-loop control module to achieve PID control, and programmable logic controller (PLC) can be connected directly with the ControlNet, such as Rockwell's PLC-5 and so on. There can be the controller PID control functions, such as Rockwell's Logix product line, it can be connected directly with the ControlNet, using the Internet to achieve its long-range control functions.1, Open-loop control systemOpen-loop control system is the object of the output (volume control) on the controller does not affect the output. In this control system, do not rely on volume will be charged back to the formation of anti-any closed-loop circuit.2, Closed-loop control systemClosed-loop control system is characterized by the output of the system object (volume control) will be sent back to the impact of anti-output controller to form one or more of the closed-loop. Closed-loop control system has positive feedback and negative feedback, if the feedback signal and the system to set the value of the signal the other hand, is referred to as negative feedback, if the same polarity is called positive feedback, the general closed-loop control system using negative feedback, also known as negative feedback control system. Closed-loop control system has many examples. For example, people with negative feedback is a closed-loop control system, the eye is the sensor to act as a feedback, the human body system through the constant variety of the right to make amendments to the final action. If there are no eyes, there is no feedback loops, it became an open-loop control system. Another example, when a full-automatic washing machine with a realcontinuously check whether the washed clothing, and wash off automatically after the power supply, it is a closed-loop control system.3, Step responseStep response refers to a step input (step function) when added to the system, the system output. Steady-state error is the system response into the steady-state, the system's desired output and actual output of the difference. The performance of control system can be stable, accurate and fast three words to describe. Stability is the stability of the system, a system must be able to work, first of all must be stable, from the step response should be a convergence point of view; quasi-control system refers to the accuracy, control precision, stability is usually state error description, it said the system output steady-state value and the difference between expectations; fast control system refers to the rapid response, and usually to a quantitative description of the rise time.4, Theory and the characteristics of PID controlIn engineering practice, the most widely used control laws regulate the proportional, integral, differential control, referred to as PID control, also known as PID regulator. PID controller has been available for nearly 70 years of history, which in its simple structure, stable, reliable, easy to adjust and become the main industrial control technologies. When charged with the structure and parameters of the object can not completely grasp, or lack of accurate mathematical model, control theory it is difficult using other techniques, the system controller structure and parameters have to rely on experience and on-site testing to determine when the application PID control of the most convenient technology. That is, when we do not fully understand the system and charged with an object, or can not be an effective means of measuring system parameters to obtain the most suitable PID control technology. PID control, in practice there are PI and PD control. PID controller is the error of the system, using proportional, integral, differential calculationfor the control of the volume control.The ratio of (P) controlProportional control is one of the most simple control methods. The controller's output and input error signal proportional to the relationship. The output has the existence of steady-state error when there is only a proportional control system.Integral (I) controlIn integral control, the controller's output and input error signal is proportional to the integral relationship. For an automatic control system, if steady-state error exists after entering the steady-state, the control system is referred to as steady-state error or having a poor system. In order to eliminate steady-state error, the controller must be the introduction of the "key points." Points of error depend on the time of the points of the increase over time, will increase the integral term. In this way, even if the error is very small, integral term will increase as time increases, it increased to promote the output of the controller so that steady-state error further reduced until zero. Therefore, the proportional + integral (PI) controller, you can make the system after entering the steady-state non-steady-state error.Differential (D) controlIn the differential control, the controller's output and input of the differential error signal (the rate of change of error) is directly proportional to the relationship. Automatic control system to overcome the errors in the adjustment process may be unstable or even oscillation. The reason is because of greater inertial components (links) or there is lag components, can inhibit the role of error, the changes always lag behind changes in error. The solution is to inhibit the changes in the role of error "in advance", that is close to zero in the error and suppress the role of error should be zero. This means that the controller only the introduction of the "proportion" of often is not enough, the proportion of the role isonly to enlarge the amplitude error, the current need to increase the "differential item" that can change the trend of prediction error, In this way, with the proportion of + differential controller, will be able to advance so that the role of inhibitory control error equal to zero or even negative, thus avoiding the amount charged with a serious overshoot. Therefore, greater inertia of the charged object or lag, the proportion of + differential (PD) controller to improve the system in the regulation of the dynamic characteristics of the process.PID控制简介当今的自动控制技术都是基于反馈的概念。

PID控制的意义中英文翻译资料

The significance of PID controlThe current level of industrial automation industries to measure the level of modernization has become an important symbol. Meanwhile, the control also experienced the development of the theory of classical control theory, modern control theory and intelligent control theory of three stages. Intelligent control is a typical example of fuzzy automatic washing machine. Open-loop control system can be divided into control systems and closed-loop control system. A control system including controller, sensors, transmitters, actuators, input and output interfaces. Controller's output through the output interface, the implementing agency, added to the charged system; control system, the amount charged, through sensors, transmitters, sent to the controller through the input interface. Different control systems, sensors, transmitters, actuators are not the same. Such as pressure control system pressure sensor to be used. Electric heating control system sensor is a temperature sensor. At present, PID control and controller or intelligent PID controller (instrument) has a lot of products have been in the engineering practice has been widely applied, there is a wide range of PID controllers, the major companies have developed PID parameter self-tuning capabilities of intelligent controller (intelligent regulator), which automatically adjusts the PID controller parameters are adjusted through the intelligent or self-correction, adaptive algorithms to achieve. PID control are achieved using pressure, temperature, flow, liquid level controller, PID control can achieve programmable controller (PLC), also allows PID control of PC systems, etc.. Programmable Logic Controller (PLC) is to use the closed-loop PID control module to achieve control, programmable logic controller (PLC) can be connected directly with ControlNet, such as Rockwell's PLC-5 and so on. PID control function also allows the controller, such as Rockwell's Logix product line, which can be connected directly with ControlNet, use the network to achieve its remote control functions.1, the open-loop control systemOpen-loop control system (open-loop control system) is charged with theobject output (controlled variables) on the controller (controller) did not affect the output. In this control system, not dependent on the amount will be charged against sending it back to form any closed loops.2, closed loop control systemClosed loop control system (closed-loop control system) is characterized by the system control object output (controlled variables) will affect the controller against the output sent back to form one or more closed loop. Closed-loop control system has positive feedback and negative feedback, if the feedback signal and system for a given value of signal contrast, is known as negative feedback (Negative Feedback), if the same polarity is called positive feedback, the general closed-loop negative feedback control systems are used , also known as negative feedback control system. Many examples of closed loop control system. Such person is a negative feedback loop control system, the eye is the sensor, as feedback, the human system through constant correction to all the right moves last. If there are no eyes, no feedback loop, will become an open-loop control system. Other cases, when a truly automatic washing machines have to continuously check whether clothes washed, and cut off the power automatically after cleaning, it is a closed loop control system.3, step responseStep response is a step input (step function) added to the system, the system outputs. Steady-state error is the response of the system into steady state, the system's expected output and actual output of the difference. Control system performance can be stable, accurate, fast and three words to describe. Stability is the stability of the system (stability), a system to work properly, first of all must be stable, from the step response appears to be that convergence; quasi-control system refers to the accuracy, control precision, usually stable state error to (Steady-state error) description, it said the system output and the expected steady-state value of the difference; faster control system response is fast, usually the rise time to quantify.4, PID control principles and characteristicsIn engineering practice, the most widely used regulator control law is proportional, integral, differential control, referred to as PID control, also known as PID regulator. PID controller has been developed for nearly 70 years, it is its simple structure, stable, reliable, easy to adjust and become one of the main techniques of industrial control. When the structure and parameters of the object and can not fully grasp, or lack of accurate mathematical models, control theory is difficult to use other technologies, the system controller structure and parameters must rely on experience and on-site commissioning to determine, when applied PID control technique is more convenient. That is, when we do not fully understand a system and the controlled object, or can not be an effective means of measurement to obtain system parameters, the most suitable PID control technology. PID control, in practice there are PI and PD control. PID controller is the error according to the system, using proportional, integral, differential calculation of the volume control to control.Proportion (P) controlProportional control is the most simple control method. The controller's output and the input error signal proportional. When only a proportional control system output when there is steady-state error (Steady-state error).Integral (I) controlIn integral control, the controller's output and the input error signal proportional to the integral. An automatic control system into the steady state if there is steady-state error, claimed that this control system is called a steady-state error or poor system (System with Steady-state Error). In order to eliminate steady state error, the controller must introduce the "integral term." Integral term of the error depends on the time integral, as time increases, integral term will increase. Thus, even if the error is very small, integral term will increase over time to increase its promotion of the controller output increases to further reduce the steady-state error, until zero. Therefore, the ratio of + integral (PI) controller allows the systemto enter steady state of no steady state error.Differential (D) controlIn the differential control, the controller output and differential input error signal (ie, rate of change of error) is proportional to. Automatic control system to overcome the errors in the adjustment process of oscillation or even instability may occur. The reason is because of greater inertia components (links), or a lag (delay) component, can inhibit the role of error, the changes always lag behind changes in the error. The solution is to change the role of inhibition of error, "ahead", that is close to zero in the error and suppress the role of error should be zero. This means that the controller only the introduction of the "ratio" item is often not enough, the proportion of item only to enlarge the role of the magnitude of the error, but now need to increase the "differential item" that can change the trend of forecast errors, In this way, with the proportion of + differential controller, it can advance to the role of inhibition of the control error is zero, even negative, thus avoiding the charged amount of serious overshoot. Therefore have greater inertia or lag the controlled object, proportional + derivative (PD) controller5, PID controller tuningTuning PID controller is the core of the control system design. It is based on the characteristics of controlled process to determine the proportion of PID controller coefficients, integral time and derivative time, the size of the. PID controller tuning are many ways to sum up, there are two categories: First, tuning the theoretical calculation. It is mainly based on the mathematical model, through theoretical calculations to determine the controller parameters. This method the calculated data may not be directly used, it must adjust and revise engineering. Second, the tuning method works, it mainly relies on engineering experience, directly in the control experiments carried out, and the method is simple, easy to master, in engineering practice is widely used. PID controller parameter tuning method works, mainly the critical ratio, reaction curve and attenuation. Three methods have their own characteristics, their common points are the experiment, and then follow theempirical formula works on the controller parameter tuning. But no matter which method used by the controller parameters are needed in the actual operation of the final adjustment and improvement. Now commonly used is the critical ratio method. PID controller using the method parameter setting of the following steps: (1) first pre-select a short enough sampling period of the system to work; (2) by adding proportional control only part until the system appears critical step response input oscillation Note the amplification factor and the proportion of time critical oscillation period; (3) a certain degree of control in the formula be adopted under the PID controller parametersPID控制的意义目前工业自动化水平已成为衡量各行各业现代化水平的一个重要标志。

PID控制中英文对照翻译

外文资料与翻译PID Contro l6.1 IntroductionThe PID controller is the most common form of feedback. It was an essential element of early governors and it became the standard tool when process control emerged in the 1940s. In process control today, more than 95% of the control loops are of PID type, most loops are actually PI control. PID controllers are today found in all areas where control is used. The controllers come in many different forms. There are standalone systems in boxes for one or a few loops, which are manufactured by the hundred thousands yearly. PID control is an important ingredient of a distributed control system. The controllers are also embedded in many special purpose control systems. PID control is often combined with logic, sequential functions, selectors, and simple function blocks to build the complicated automation systems used for energy production, transportation, and manufacturing. Many sophisticated control strategies, such as model predictive control, are also organized hierarchically. PID control is used at the lowest level; the multivariable controller gives the set points to the controllers at the lower level. The PID controller can thus be said to be the “bread and butter of control engineering. It is an important component in every control engineer’s tool box.PID controllers have survived many changes in technology, from mechanics and pneumatics to microprocessors via electronic tubes, transistors, integrated circuits. The microprocessor has had a dramatic influence the PID controller. Practically all PID controllers made today are based on microprocessors. This has given opportunities to provide additional features like automatic tuning, gain scheduling, and continuous adaptation.6.2 AlgorithmWe will start by summarizing the key features of the PID controller. The “textbook” version of the PID algorithm is described by:()()()()⎪⎪⎭⎫ ⎝⎛++=⎰dt t de d e t e K t u T T d t i 01ττ 6.1 where y is the measured process variable, r the reference variable, u is the control signal and e is the control error （e =sp y − y ）. The reference variable is often calledthe set point. The control signal is thus a sum of three terms: the P-term （which is proportional to the error）, the I-term （which is proportional to the integral of the error）, and the D-term （which is proportional to the derivative of the error）. The controller parameters are proportional gain K, integral time T i, and derivative time T d. The integral, proportional and derivative part can be interpreted as control actions based on the past, the present and the future as is illustrated in Figure 2.2. The derivative part can also be interpreted as prediction by linear extrapolation as is illustrated in Figure 2.2. The action of the different terms can be illustrated by the following figures which show the response to step changes in the reference value in a typical case.Effects of Proportional, Integral and Derivative ActionProportional control is illustrated in Figure 6.1. The controller is given by D6.1E with T i= and T d=0. The figure shows that there is always a steady state error in proportional control. The error will decrease with increasing gain, but the tendency towards oscillation will also increase.Figure 6.2 illustrates the effects of adding integral. It follows from D6.1E that the strength of integral action increases with decreasing integral time T i. The figure shows that the steady state error disappears when integral action is used. Compare with the discussion of the “magic of integral action” in Section 2.2. The tendency for oscillation also increases with decreasing T i. The properties of derivative action are illustrated in Figure 6.3.Figure 6.3 illustrates the effects of adding derivative action. The parameters K and T i are chosen so that the closed loop system is oscillatory. Damping increases with increasing derivative time, but decreases again when derivative time becomes too large. Recall that derivative action can be interpreted as providing prediction by linear extrapolation over the time T d. Using this interpretation it is easy to understand that derivative action does not help if the prediction time T d is too large. In Figure 6.3 the period of oscillation is about 6 s for the system without derivative Chapter 6. PID ControlFigure 6.1Figure 6.2Derivative actions cease to be effective when T d is larger than a 1 s (one sixth of the period). Also notice that the period of oscillation increases when derivative time is increased.A PerspectiveThere is much more to PID than is revealed by （6.1）. A faithful implementation of the equation will actually not result in a good controller. To obtain a good PID controller it is also necessary to consider。

【系统】PID中英文对照翻译

【关键字】系统PID ControlIntroductionThe PID controller is the most common form of feedback. It was an essential element of early governors and it became the standard tool when process control emerged in the 1940s. In process control today, more than 95% of the control loops are of PID type, most loops are actually PI control. PID controllers are today found in all areas where control is used. The controllers come in many different forms. There are standalone systems in boxes for one or a few loops, which are manufactured by the hundred thousands yearly. PID control is an important ingredient of a distributed control system. The controllers are also embedded in many special purpose control systems. PID control is often combined with logic, sequential functions, selectors, and simple function blocks to build the complicated automation systems used for energy production, transportation, and manufacturing. Many sophisticated control strategies, such as model predictive control, are also organized hierarchically. PID control is used at the lowest level; the multivariable controller gives the set points to the controllers at the lower level. The PID controller can thus be said to be the “bread a nd butter of control engineering. It is an important component in every control engineer’s tool box.PID controllers have survived many changes in technology, from mechanics and pneumatics to microprocessors via electronic tubes, transistors, integrated circuits. The microprocessor has had a dramatic influence the PID controller. Practically all PID controllers made today are based on microprocessors. This has given opportunities to provide additional features like automatic tuning, gain scheduling, and continuous adaptation.6.2 The AlgorithmWe will start by summarizing the key features of the PID controller. The “textbook” version of the PID algorithm is described by:6.1where y is the measured process variable, r the reference variable, u is the control signal and e is the control error（e = − y）. The reference variable is often called the set point. The control signal is thus a sum of three terms: the P-term （which is proportional to the error）, the I-term （which is proportional to the integral of the error）, and the D-term （which is proportional to the derivative of the error）. Thecontroller parameters are proportional gain K, integral time Ti, and derivative time Td. The integral, proportional and derivative part can be interpreted as control actions based on the past, the present and the future as is illustrated in Figure 2.2. The derivative part can also be interpreted as prediction by linear extrapolation as is illustrated in Figure 2.2. The action of the different terms can be illustrated by the following figures which show the response to step changes in the reference value in a typical case.Effects of Proportional, Integral and Derivative ActionProportional control is illustrated in Figure 6.1. The controller is given by D6.1E with Ti = and Td=0. The figure shows that there is always a steady state error in proportional control. The error will decrease with increasing gain, but the tendency towards oscillation will also increase.Figure 6.2 illustrates the effects of adding integral. It follows from D6.1E that the strength of integral action increases with decreasing integral time Ti. The figure shows that the steady state error disappears when integral action is used. Compare with the discussion of the “magic of integral action” in Section 2.2. The tendency for oscillation also increases with decreasing Ti. The properties of derivative action are illustrated in Figure 6.3.Figure 6.3 illustrates the effects of adding derivative action. The parameters K and Ti are chosen so that the closed loop system is oscillatory. Damping increases with increasing derivative time, but decreases again when derivative time becomes too large. Recall that derivative action can be interpreted as providing prediction by linear extrapolation over the time Td. Using this interpretation it is easy to understand that derivative action does not help if the prediction time Td is too large. In Figure 6.3 the period of oscillation is about 6 s for the system without derivative Chapter 6. PID ControlFigure 6.1Figure 6.2Derivative actions cease to be effective when Td is larger than a 1 s (one sixth of the period). Also notice that the period of oscillation increases when derivative time is increased.A PerspectiveThere is much more to PID than is revealed by （6.1）. A faithful implementation of the equation will actually not result in a good controller. To obtain a good PID controller it is also necessary to consider。

外文翻译---PID控制算法

附录附件1：英文资料及中文翻译英文资料PID control algorithm1, PID is a closed loop control algorithm. Therefore, to achieve the PID algorithm, you must have the hardware loop control, that is, must have feedback. Such as controlling a motor speed, you have to have a measurement of speed sensor, and the results fed back to the control line, the following will also be speed control, for example.2, PID is the proportional (P), integral (I), Derivative (D) control algorithms. But not must have these three algorithms, it can be PD, PI, or even only the P-control algorithm. I used for closed-loop control of one of the most simple idea only P control, the current results fed back, and then subtract the target, is positive, then slow down, then it is negative acceleration. Now know that this is just the simplest closed-loop control algorithms.3, the ratio (P), integral (I), differential (D) control algorithms have effect:?????Proportion of the reaction system, the basic (current) deviation e (t), coefficient, can speed up the regulation, reduce errors, but the system is too large proportion decreased stability, or even cause system instability;?????Integral, the cumulative deviation of the reaction system, allowing the system to eliminate the steady state error, no gradient to improve, because there is an error, integral control will be carried out until no error;?????Differential, reflecting the rate of change of the system deviation signal e (t)-e (t-1), is foreseen to anticipate trends deviation generated ahead of the control action, the deviation is not formed, has been the differential regulation of remove, so you can improve the dynamic performance. However, differential interference to noise amplification role in strengthening differential interference detrimental to the system.Integral and differential can not work alone and must be proportional control cooperation.4, the controller of the P, I, D item selection.The following rules will be used to control various control features briefly summarized here: 1, the proportional control law P: P control law used to quickly overcome the disturbances, which act on the output value of the fast, but not very stable at a desired value, compared with a negative result is to effectively overcome the disturbances, but more than a difference appears. It is suitable for the control channel lag is small, little change in the load control is less demanding, controlled parameters within a certain range to allow more than poor occasions. Such as: Jin Biao under the Ministry of Public Works pump house cold, heat pool water level control; pump room intermediate tank oil level control. 2, proportional integral control law (PI): in engineering proportional integral control law is the most widely used control law. Points that can be eliminated on the basis of the proportion of residual error, which applies to the control channel lag is small, little change in load, was charged with parameters do not allow more than a poor occasions. Eg: In the main chamber kiln heavy commutation No. F1419 F1401 to heavy gun flow control system; pump room for the pipeline flow control system; annealing furnace temperature control system, etc. districts. 3, proportional and differential control law (PD): Differential effects has advanced, with capacity for hysteresis control channel, the introduction of differential involved in the control, the derivative term is set properly in the case, for improving the system's dynamic performance indicators, have a significant effect. Therefore, for the time constant of the control channel or capacity lags larger occasions, in order to improve the stability of the system, reducing the dynamic deviation proportional derivative control law can be used. Such as: heated temperature control, composition control. It should be noted that, for those pure lag larger area, the derivative term is powerless, and in the measured signal is noisy or periodic vibration system, you should not use derivative control. Such as: large kiln glass level control. 4, for example, integral derivative control laws (PID): PID control law is an ideal control law, it is based on the introduction of proportional integral, can eliminate residual error, then add the derivative action, but also improve system stability. Itis suitable for the control channel or capacity lag time constant is large, the control requirements of the occasion. Such as temperature control, composition control.????Given D role of the law, we must also understand the concept of time lag, the time lag including capacity lag and pure lag. Capacity in which they lag usually include: measurement and transmission lag lag. Measurement hysteresis is detecting element in the detection need to establish a balance, such as thermocouples, RTD, pressure, etc. resulting from a slow response lag. The transmission lag is in the sensors, transmitters, actuators and other devices produce a control delay. Pure delay is measured relative lag in industry, most of the time delay caused due to material transport, such as: large kiln glass level, feeding maneuver done in the nuclear level gauge detection requires a very long period of time.????In short, the choice of control law according to the process characteristics and process requirements to select, and never say PID control law in any case, has better control performance, regardless of the occasion are used is unwise. If you do so, it will only add complexity to the other work and give parameter tuning difficult. When using PID controller has not yet reached technological requirements, you need to consider other control scheme. If cascade control, feedforward control, large hysteresis control.5the problem. Kp, Ti, Td setting the three parameters of the PID control algorithm key issues. Generally they can only be set when programming the approximate value and the system is running through repeated testing to determine the best value. Therefore, the program must apply for the commissioning phase and memory can be modified at any of these three parameters.6 the parameter self-tuning. In some applications, such as generic instrumentation industry, systems work object is uncertain, different objects have different parameter values, can not set parameters for the user, on the introduction of the concept of self-tuning parameters . Essence is the first time, through the N measurements is looking for a new job object parameters, andremembered as a basis for future work.中文翻译PID控制算法1，PID是一个闭环控制算法。

机械类数控外文翻译外文文献英文文献 PID控制器

PID controllerA proportional–integral–derivative controller (PID controller) is a generic .control loop feedback mechanism widely used in industrial control systems. A PID controller attempts to correct the error between a measured process variable and a desired setpoint by calculating and then outputting a corrective action that can adjust the process accordingly.The PID controller calculation (algorithm) involves three separate parameters; the Proportional, the Integral and Derivative values. The Proportional value determines the reaction to the current error, the Integral determines the reaction based on the sum of recent errors and the Derivative determines the reaction to the rate at which the error has been changing. The weightedsum of these three actions is used to adjust the process via a control element such as the position of a control valve or the power supply of a heating element.By "tuning" the three constants in the PID controller algorithm the PID can provide control action designed for specific process requirements. The response of the controller can be described in terms of the responsiveness of the controller to an error, the degree to which the controller overshoots the setpoint and the degree of system oscillation. Note that the use of the PID algorithm for control does not guarantee optimal control of the system or system stability.Some applications may require using only one or two modes to provide the appropriate system control. This is achieved by setting the gain of undesired control outputs to zero. A PID controller will be called a PI, PD, P or I controller in the absence of the respective control actions. PI controllers are particularly common, since derivative action is very sensitive to measurement noise, and the absence of an integral value may prevent the system from reaching its target value due to the control action.Note: Due to the diversity of the field of control theory and application, many naming conventions for the relevant variables are in common use.1.Control loop basicsA familiar example of a control loop is the action taken to keep one's shower water at the ideal temperature, which typically involves the mixing of two process streams, cold and hot water. The person feels the water to estimate its temperature. Based on this measurement they perform a control action: use the cold water tap to adjust the process. The person would repeat this input-output control loop, adjusting the hot water flow until the process temperature stabilized at the desired value.Feeling the water temperature is taking a measurement of the process value or process variable (PV). The desired temperature is called the setpoint (SP). The output from the controller and input to the process (the tap position) is called the manipulated variable (MV). The difference between the measurement and the setpoint is the error (e), too hot or too cold and by how much.As a controller, one decides roughly how much to change the tap position (MV) after one determines the temperature (PV), and therefore the error. This first estimate is the equivalent of the proportional action of a PID controller. The integral action of a PID controller can be thought of as gradually adjusting the temperature when it is almost right. Derivative action can be thought of as noticing the water temperature is getting hotter or colder, and how fast, and taking that into account when deciding how to adjust the tap.Making a change that is too large when the error is small is equivalent to a high gain controller and will lead to overshoot. If the controller were to repeatedly make changes that were too large and repeatedly overshoot the target, this control loop would be termed unstable and the output would oscillate around the setpoint in either a constant, growing, or decaying sinusoid. A human would not do this because we are adaptive controllers, learning from the process history, but PID controllers do not have the ability to learn and must be set up correctly. Selectingthe correct gains for effective control is known as tuning the controller.If a controller starts from a stable state at zero error (PV = SP), then further changes by the controller will be in response to changes in other measured or unmeasured inputs to the process that impact on the process, and hence on the PV. Variables that impact on the process other than the MV are known as disturbances and generally controllers are used to reject disturbances and/or implement setpoint changes. Changes in feed water temperature constitute a disturbance to the shower process.In theory, a controller can be used to control any process which has a measurable output (PV), a known ideal value for that output (SP) and an input to the process (MV) that will affect the relevant PV. Controllers are used in industry to regulate temperature, pressure, flow rate, chemical composition, speed and practically every other variable for which a measurement exists. Automobile cruise control is an example of a process which utilizes automated control.Due to their long history, simplicity, well grounded theory and simple setup and maintenance requirements, PID controllers are the controllers of choice for many of these applications.2.PID controller theoryNote: This section describes the ideal parallel or non-interacting form of the PID controller. For other forms please see the Section "Alternative notation and PID forms".The PID control scheme is named after its three correcting terms, whose sum constitutes the manipulated variable (MV). Hence:where Pout, Iout, and Dout are the contributions to the output from the PID controller from each of the three terms, as defined below.2.1. Proportional termThe proportional term makes a change to the output that is proportional to the current error value. The proportional response can be adjusted by multiplying the error by a constant Kp, called the proportional gain.The proportional term is given by:WherePout: Proportional outputKp: Proportional Gain, a tuning parametere: Error = SP − PVt: Time or instantaneous time (the present)Change of response for varying KpA high proportional gain results in a large change in the output for a given change in the error. If the proportional gain is too high, the system can become unstable (See the section on Loop Tuning). In contrast, a small gain results in a small output response to a large input error, and a less responsive (or sensitive) controller. If the proportional gain is too low, the control action may be too small when responding to system disturbances.In the absence of disturbances, pure proportional control will not settle at its target value, but will retain a steady state error that is a function of the proportional gain and the process gain. Despite the steady-state offset, both tuning theory and industrial practice indicate that it is the proportional term that should contribute the bulk of the output change.2.2.Integral termThe contribution from the integral term is proportional to both the magnitude of the error and the duration of the error. Summing the instantaneous error over time (integrating the error) gives the accumulated offset that should have been corrected previously. The accumulated error is then multiplied by the integral gain and added to the controller output. The magnitude of the contribution of the integral term to the overall control action is determined by the integral gain, Ki.The integral term is given by:Iout: Integral outputKi: Integral Gain, a tuning parametere: Error = SP − PVτ: Time in the past contributing to the integral responseThe integral term (when added to the proportional term) accelerates the movement of the process towards setpoint and eliminates the residual steady-state error that occurs with a proportional only controller. However, since the integral term is responding to accumulated errors from the past, it can cause the present value to overshoot the setpoint value (cross over the setpoint and then create a deviation in the other direction). For further notes regarding integral gain tuning and controller stability, see the section on loop tuning.2.3 Derivative termThe rate of change of the process error is calculated by determining the slope of the error over time (i.e. its first derivative with respect to time) and multiplying this rate of change by the derivative gain Kd. The magnitude of the contribution of the derivative term to the overall control action is termed the derivative gain, Kd.The derivative term is given by:Dout: Derivative outputKd: Derivative Gain, a tuning parametere: Error = SP − PVt: Time or instantaneous time (the present)The derivative term slows the rate of change of the controller output and this effect is most noticeable close to the controller setpoint. Hence, derivative control is used to reduce the magnitude of the overshoot produced by the integral component and improve the combined controller-process stability. However, differentiation of a signal amplifies noise and thus this term in the controller is highly sensitive to noise in the error term, and can cause a process to become unstable if the noise and the derivative gain are sufficiently large.2.4 SummaryThe output from the three terms, the proportional, the integral and the derivative terms are summed to calculate the output of the PID controller. Defining u(t) as the controller output, the final form of the PID algorithm is:and the tuning parameters areKp: Proportional Gain - Larger Kp typically means faster response since thelarger the error, the larger the Proportional term compensation. An excessively large proportional gain will lead to process instability and oscillation.Ki: Integral Gain - Larger Ki implies steady state errors are eliminated quicker. The trade-off is larger overshoot: any negative error integrated during transient response must be integrated away by positive error before we reach steady state.Kd: Derivative Gain - Larger Kd decreases overshoot, but slows down transient response and may lead to instability due to signal noise amplification in the differentiation of the error.3. Loop tuningIf the PID controller parameters (the gains of the proportional, integral and derivative terms) are chosen incorrectly, the controlled process input can be unstable, i.e. its output diverges, with or without oscillation, and is limited only by saturation or mechanical breakage. Tuning a control loop is the adjustment of its control parameters (gain/proportional band, integral gain/reset, derivative gain/rate) to the optimum values for the desired control response.The optimum behavior on a process change or setpoint change varies depending on the application. Some processes must not allow an overshoot of the process variable beyond the setpoint if, for example, this would be unsafe. Other processes must minimize the energy expended in reaching a new setpoint. Generally, stability of response (the reverse of instability) is required and the process must not oscillate for any combination of process conditions and setpoints. Some processes have a degree of non-linearity and so parameters that work well at full-load conditions don't work when the process is starting up from no-load. This section describes some traditional manual methods for loop tuning.There are several methods for tuning a PID loop. The most effective methods generally involve the development of some form of process model, then choosing P, I, and D based on the dynamic model parameters. Manual tuning methods can be relatively inefficient.The choice of method will depend largely on whether or not the loop can be taken "offline" for tuning, and the response time of the system. If the system can be taken offline, the best tuning method often involves subjecting the system to a step change in input, measuring the output as a function of time, and using this response to determine the control parameters.Choosing a Tuning MethodMethodAdvantagesDisadvantagesManual TuningNo math required. Online method.Requires experiencedpersonnel.Ziegler–NicholsProven Method. Online method.Process upset, sometrial-and-error, very aggressive tuning.Software ToolsConsistent tuning. Online or offline method. May includevalve and sensor analysis. Allow simulation before downloading.Some costand training involved.Cohen-CoonGood process models.Some math. Offline method. Only good forfirst-order processes.3.1 Manual tuningIf the system must remain online, one tuning method is to first set the I and D values to zero. Increase the P until the output of the loop oscillates, then the P should be left set to be approximately half of that value for a "quarter amplitude decay" type response. Then increase D until any offset is correct in sufficienttime for the process. However, too much D will cause instability. Finally, increase I, if required, until the loop is acceptably quick to reach its reference after a load disturbance. However, too much I will cause excessive response and overshoot. A fast PID loop tuning usually overshoots slightly to reach the setpoint more quickly; however, some systems cannot accept overshoot, in which case an "over-damped" closed-loop system is required, which will require a P setting significantly less than half that of the P setting causing oscillation.3.2Ziegler–Nichols methodAnother tuning method is formally known as the Ziegler–Nichols method, introduced by John G. Ziegler and Nathaniel B. Nichols. As in the method above, the I and D gains are first set to zero. The "P" gain is increased until it reaches the "critical gain" Kc at which the output of the loop starts to oscillate. Kc and the oscillation period Pc are used to set the gains as shown:3.3 PID tuning softwareMost modern industrial facilities no longer tune loops using the manual calculation methods shown above. Instead, PID tuning and loop optimization software are used to ensure consistent results. These software packages will gather the data, develop process models, and suggest optimal tuning. Some software packages can even develop tuning by gathering data from reference changes.Mathematical PID loop tuning induces an impulse in the system, and then uses the controlled system's frequency response to design the PID loop values. In loops with response times of several minutes, mathematical loop tuning is recommended, because trial and error can literally take days just to find a stable set of loop values. Optimal values are harder to find. Some digital loop controllers offer a self-tuning feature in which very small setpoint changes are sent to the process, allowing the controller itself to calculate optimal tuning values.Other formulas are available to tune the loop according to different performance criteria.4 Modifications to the PID algorithmThe basic PID algorithm presents some challenges in control applications that have been addressed by minor modifications to the PID form.One common problem resulting from the ideal PID implementations is integralwindup. This can be addressed by:Initializing the controller integral to a desired valueDisabling the integral function until the PV has entered the controllable regionLimiting the time period over which the integral error is calculatedPreventing the integral term from accumulating above or below pre-determined boundsMany PID loops control a mechanical device (for example, a valve). Mechanical maintenance can be a major cost and wear leads to control degradation in the form of either stiction or a deadband in the mechanical response to an input signal. The rate of mechanical wear is mainly a function of how often a device is activated to make a change. Where wear is a significant concern, the PID loop may have an output deadband to reduce the frequency of activation of the output (valve). This is accomplished by modifying the controller to hold its output steady if the change would be small (within the defined deadband range). The calculated output must leave the deadband before the actual output will change.The proportional and derivative terms can produce excessive movement in the output when a system is subjected to an instantaneous "step" increase in the error, such as a large setpoint change. In the case of the derivative term, this is due to taking the derivative of the error, which is very large in the case of an instantaneous step change.5. Limitations of PID controlWhile PID controllers are applicable to many control problems, they can perform poorly in some applications.PID controllers, when used alone, can give poor performance when the PID loop gains must be reduced so that the control system does not overshoot, oscillate or "hunt" about the control setpoint value. The control system performance can be improved by combining the feedback (or closed-loop) control of a PID controller with feed-forward (or open-loop) control. Knowledge about the system (such as the desired acceleration and inertia) can be "fed forward" and combined with the PID output to improve the overall system performance. The feed-forward value alone can often provide the major portion of the controller output. The PID controller can then be used primarily to respond to whatever difference or "error" remains between the setpoint (SP) and the actual value of the process variable (PV). Since the feed-forward output is not affected by the process feedback, it can never cause the control system to oscillate, thus improving the system response and stability.For example, in most motion control systems, in order to accelerate a mechanical load under control, more force or torque is required from the prime mover, motor, or actuator. If a velocity loop PID controller is being used to control the speed of the load and command the force or torque being applied by the prime mover, then it is beneficial to take the instantaneous acceleration desired for the load, scale that value appropriately and add it to the output of the PID velocity loop controller. This means that whenever the load is being accelerated or decelerated, a proportional amount of force is commanded from the prime mover regardless of the feedback value. The PID loop in this situation uses the feedback information to effect any increase or decrease of the combined output in order to reduce the remaining difference between the process setpoint and thefeedback value. Working together, the combined open-loop feed-forward controller and closed-loop PID controller can provide a more responsive, stable and reliable control system.Another problem faced with PID controllers is that they are linear. Thus, performance of PID controllers in non-linear systems (such as HV AC systems) is variable. Often PID controllers are enhanced through methods such as PID gain scheduling or fuzzy logic. Further practical application issues can arise from instrumentation connected to the controller. A high enough sampling rate, measurement precision, and measurement accuracy are required to achieve adequate control performance.A problem with the Derivative term is that small amounts of measurement or process noise can cause large amounts of change in the output. It is often helpful to filter the measurements with a low-pass filter in order to remove higher-frequency noise components. However, low-pass filtering and derivative control can cancel each other out, so reducing noise by instrumentation means is a much better choice. Alternatively, the differential band can be turned off in many systems with little loss of control. This is equivalent to using the PID controller as a PI controller.6. Cascade controlOne distinctive advantage of PID controllers is that two PID controllers can be used together to yield better dynamic performance. This is called cascaded PID control. In cascade control there are two PIDs arranged with one PID controlling the set point of another. A PID controller acts as outer loop controller, which controls the primary physical parameter, such as fluid level or velocity. The other controller acts as inner loop controller, which reads the output of outer loop controller as set point, usually controlling a more rapid changing parameter, flowrate or accelleration. It can be mathematically proved that the working frequency of the controller is increased and the time constant of the object is reduced by using cascaded PID controller.[vague]7. Physical implementation of PID controlIn the early history of automatic process control the PID controller was implemented as a mechanical device. These mechanical controllers used a lever, spring and a mass and were often energized by compressed air. These pneumatic controllers were once the industry standard.Electronic analog controllers can be made from a solid-state or tube amplifier, a capacitor and a resistance. Electronic analog PID control loops were often found within more complex electronic systems, for example, the head positioning of a disk drive, the power conditioning of a power supply, or even the movement-detection circuit of a modern seismometer. Nowadays, electronic controllers have largely been replaced by digital controllers implemented with microcontrollers or FPGAs.Most modern PID controllers in industry are implemented in software in programmable logic controllers (PLCs) or as a panel-mounted digital controller. Software implementations have the advantages that they are relatively cheap and are flexible with respect to the implementation of the PID algorithm.PID控制器比例积分微分控制器（PID调节器）是一个控制环，广泛地应用于工业控制系统里的反馈机制。

自动化专业外文翻译----温度控制简介和PID控制器

毕业设计（论文）外文资料翻译系别：电气工程系专业：电气工程及其自动化班级：姓名：学号：外文出处：Specialized English For ArchitecturalElectric Engineering and Automation附件：1、外文原文；2、外文资料翻译译文。

1、外文原文Introductions to temperature control and PID controllersProcess control system.Automatic process control is concerned with maintaining process variables temperatures pressures flows compositions, and the like at some desired operation value. Processes are dynamic in nature. Changes are always occurring, and if actions are nottaken, the important process variables-those related to safety, product quality, and production rates-will not achieve design conditions.In order to fix ideas, let us consider a heat exchanger in which a process stream is heated by condensing steam. The process is sketched in Fig.1Fig. 1 Heat exchangerThe purpose of this unit is to heat the process fluid from some inlet temperature, Ti(t), up to a certain desired outlet temperature, T(t). As mentioned, the heating medium is condensing steam.The energy gained by the process fluid is equal to the heat released by the steam, provided there are no heat losses to surroundings, iii that is, the heat exchanger andpiping are well insulated.In this process there are many variables that can change, causing the outlet temperature to deviate from its desired value. [21 If this happens, some action must be taken to correct for this deviation. That is, the objective is to control the outlet process temperature to maintain its desired value.One way to accomplish this objective is by first measuring the temperature T(t) , then comparing it to its desired value, and, based on this comparison, deciding what to do to correct for any deviation. The flow of steam can be used to correct for the deviation. This is, if the temperature is above its desired value, then the steam valve can be throttled back to cut the stearr flow (energy) to the heat exchanger. If the temperature is below its desired value, then the steam valve could be opened some more to increase the steam flow (energy) to the exchanger. All of these can be done manually by the operator, and since the procedure is fairly straightforward, it should present no problem. However, since in most process plants there are hundreds of variables that must be maintained at some desired value, this correction procedure would required a tremendous number of operators. Consequently, we would like to accomplish this control automatically. That is, we want to have instnnnents that control the variables wJtbom requ)ring intervention from the operator. (si This is what we mean by automatic process control.To accomplish ~his objective a control system must be designed and implemented.A possible control system and its basic components are shown in Fig.2.Fig. 2 Heat exchanger control loopThe first thing to do is to measure the outlet temperaVare of the process stream. A sensor (thermocouple, thermistors, etc) does this. This sensor is connected physically to a transmitter, which takes the output from the sensor and converts it to a signal strong enough to be transmitter to a controller. The controller then receives the signal, which is related to the temperature, and compares it with desired value. Depending on this comparison, the controller decides what to do to maintain the temperature at its desired value. Base on this decision, the controller then sends another signal to final control element, which in turn manipulates the steam flow.The preceding paragraph presents the four basic components of all control systems. They are(1) sensor, also often called the primary element.(2) transmitter, also called the secondary element.(3) controller, the "brain" of the control system.(4) final control system, often a control valve but not always. Other common final control elements are variable speed pumps, conveyors, and electric motors.The importance of these components is that they perform the three basic operations that must be present in every control system. These operations are(1) Measurement (M) : Measuring the variable to be controlled is usually done bythe combination of sensor and transmitter.(2) Decision (D): Based on the measurement, the controller must then decide what to do to maintain the variable at its desired value.(3) Action (A): As a result of the controller's decision, the system must then take an action. This is usually accomplished by the final control element.As mentioned, these three operations, M, D, and A, must be present in every control system.PID controllers can be stand-alone controllers (also called single loop controllers), controllers in PLCs, embedded controllers, or software in Visual Basic or C# computer programs.PID controllers are process controllers with the following characteristics:Continuous process controlAnalog input (also known as "measuremem" or "Process Variable" or "PV")Analog output (referred to simply as "output")Setpoint (SP)Proportional (P), Integral (I), and/or Derivative (D) constantsExamples of "continuous process control" are temperature, pressure, flow, and level control. For example, controlling the heating of a tank. For simple control, you have two temperature limit sensors (one low and one high) and then switch the heater on when the low temperature limit sensor tums on and then mm the heater off when the temperature rises to the high temperature limit sensor. This is similar to most home air conditioning & heating thermostats.In contrast, the PID controller would receive input as the actual temperature and control a valve that regulates the flow of gas to the heater. The PID controller automatically finds the correct (constant) flow of gas to the heater that keeps the temperature steady at the setpoint. Instead of the temperature bouncing back and forth between two points, the temperature is held steady. If the setpoint is lowered, then the PID controller automatically reduces the amount of gas flowing to the heater. If the setpoint is raised, then the PID controller automatically increases the amount of gas flowing to the heater. Likewise the PID controller would automatically for hot, sunnydays (when it is hotter outside the heater) and for cold, cloudy days.The analog input (measurement) is called the "process variable" or "PV". You want the PV to be a highly accurate indication of the process parameter you are trying to control. For example, if you want to maintain a temperature of + or -- one degree then we typically strive for at least ten times that or one-tenth of a degree. If the analog input is a 12 bit analog input and the temperature range for the sensor is 0 to 400 degrees then our "theoretical" accuracy is calculated to be 400 degrees divided by 4,096 (12 bits) =0.09765625 degrees. [~] We say "theoretical" because it would assume there was no noise and error in our temperature sensor, wiring, and analog converter. There are other assumptions such as linearity, etc.. The point being--with 1/10 of a degree "theoretical" accuracy--even with the usual amount of noise and other problems-- one degree of accuracy should easily be attainable.The analog output is often simply referred to as "output". Often this is given as 0~100 percent. In this heating example, it would mean the valve is totally closed (0%) or totally open (100%).The setpoint (SP) is simply--what process value do you want. In this example--what temperature do you want the process at?The PID controller's job is to maintain the output at a level so that there is no difference (error) between the process variable (PV) and the setpoint (SP).In Fig. 3, the valve could be controlling the gas going to a heater, the chilling of a cooler, the pressure in a pipe, the flow through a pipe, the level in a tank, or any other process control system. What the PID controller is looking at is the difference (or "error") between the PV and the SP.P，I，&DDifference error PID controlprocessvariableFig .3 PIDcontrolIt looks at the absolute error and the rate of change of error. Absolute error means--is there a big difference in the PV and SP or a little difference? Rate of change of error means--is the difference between the PV or SP getting smaller or larger as time goes on.When there is a "process upset", meaning, when the process variable or the setpoint quickly changes--the PID controller has to quickly change the output to get the process variable back equal to the setpoint. If you have a walk-in cooler with a PID controller and someone opens the door and walks in, the temperature (process variable) could rise very quickly. Therefore the PID controller has to increase the cooling (output) to compensate for this rise in temperature.Once the PID controller has the process variable equal to the setpoint, a good PID controller will not vary the output. You want the output to be very steady (not changing) . If the valve (motor, or other control element) is constantly changing, instead of maintaining a constant value, this could cause more wear on the control element.So there are these two contradictory goals. Fast response (fast change in output) when there is a "process upset", but slow response (steady output) when the PV is close to the setpoint.Note that the output often goes past (over shoots) the steady-state output to get the process back to the setpoint. For example, a cooler may normally have its cooling valve open 34% to maintain zero degrees (after the cooler has been closed up and the temperature settled down). If someone opens the cooler, walks in, walks around to find something, then walks back out, and then closes the cooler door--the PID controller is freaking out because the temperature may have raised 20 degrees! So it may crank the cooling valve open to 50, 75, or even 100 percent--to hurry up and cool the cooler back down--before slowly closing the cooling valve back down to 34 percent.Let's think about how to design a PID controller.We focus on the difference (error) between the process variable (PV) and the setpoint (SP). There are three ways we can view the error.The absolute errorThis means how big is the difference between the PV and SP. If there is a small difference between the PV and the SP--then let's make a small change in the output. If there is a large difference in the PV and SP--then let's make a large change in the output. Absolute error is the "proportional" (P) component of the PID controller.The sum of errors over timeGive us a minute and we will show why simply looking at the absolute error (proportional) only is a problem. The sum of errors over time is important and is called the "integral" (I) component of the PID controller. Every time we run the PID algorithm we add the latest error to the sum of errors. In other words Sum of Errors = Error 1 q- Error2 + Error3 + Error4 + ....The dead timeDead time refers to the delay between making a change in the output and seeing the change reflected in the PV. The classical example is getting your oven at the right temperature. When you first mm on the heat, it takes a while for the oven to "heat up". This is the dead time. If you set an initial temperature, wait for the oven to reach the initial temperature, and then you determine that you set the wrong temperature--then it will take a while for the oven to reach the new temperature setpoint. This is also referred to as the "derivative" (D) component of the PID controller. This holds some future changes back because the changes in the output have been made but are not reflected in the process variable yet.Absolute Error/ProportionalOne of the first ideas people usually have about designing an automatic process controller is what we call "proportional". Meaning, if the difference between the PV and SP is small--then let's make a small correction to the output. If the difference between the PV and SP is large-- then let's make a larger correction to the output. Thisidea certainly makes sense.We simulated a proportional only controller in Microsoft Excel. Fig.4 is the chart showing the results of the first simulation (DEADTIME = 0, proportional only): Proportional and Integral ControllersThe integral portion of the PID controller accounts for the offset problem in a proportional only controller. We have another Excel spreadsheet that simulates a PID controller with proportional and integral control. Here (Fig. 5) is a chart of the first simulation with proportional and integral (DEADTIME :0, proportional = 0.4).As you can tell, the PI controller is much better than just the P controller. However, dead time of zero (as shown in the graph) is not common.Fig .4 The simulation chartDerivative ControlDerivative control takes into consideration that if you change the output, then it takes tim for that change to be reflected in the input (PV).For example, let's take heating of the oven.Fig.5The simulation chartIf we start turning up the gas flow, it will take time for the heat to be produced, the heat to flow around the oven, and for the temperature sensor to detect the increased heat. Derivative control sort of "holds back" the PID controller because some increase in temperature will occur without needing to increase the output further. Setting the derivative constant correctly allows you to become more aggressive with the P & Iconstants.2、外文资料翻译译文温度控制简介和PID控制器过程控制系统自动过程控制系统是指将被控量为温度、压力、流量、成份等类型的过程变量保持在理想的运行值的系统。

智能小车控制中模糊-PID控制的实现-毕业设计英文翻译

Implementation of Fuzzy-PID in Smart Car Control Abstract—An unmanued smart car control system and the fuzzy-PID control algorithm are produced . A design scheme of fuzzy-PID controller is put forward. The simulation analysis from matlab indicated that the dynamic performance of fuzzy-PID control algorithm is better than that of usual PID. Experimental result of smart car show that it can follow the black guide line well and fast-stable complete running the whole trip. Keywords — fuzzy-PID; smart car; fuzzy controller; fuzzy control1 IntroductionIn recent years, many countries are developing unmanned vehicle technology. This gives birth to many new theories and applied technology. Reference[1] presents the theory of turn ahead which uses real-time monitoring speed to change the turn-in point dynamically, then it implements the control strategy to achieve a perfect characteristics of steering. Reference[2] uses edge detection algorithm to extract track information and adopt P control. Reference[3]proposes a efficient, good anti-jamming and adaptive image processing dynamic algorithm which effectively solves the out of track caused by the changes of ambient light and track. Reference[4] reconstructs spatial relationships of track and calibrates camera using nonlinear optimization, then it can measure lateral deviation accurately. The above improve vehicle performance in one way but they are all lack of characteristics of car movement and based on lots of experiments. A fuzzy-PID control algorithm and a design scheme of fuzzy-PID controller are put forward in this paper. At last, the experimental result is given out to prove the validity of fuzzy-PID.2 Hardware system designTo implement the design of fuzzy-PID algorithm, it’s necessary to design a hardware system of smart car. Smart car would have a smart control unite which contain detection of guide line, steering angle value, speed value and so on. See details in Fig.1.Fig.1 The functional block diagram of smart car3 Basic principle of fuzzy-PIDIt’s difficult for usual PID control algorithm to achieve the best effect. Because, the parameters Kp, Ki, Kd can’t adjust to different object or different state of the same object. Fuzzy control is based on fuzzy set and fuzzy logic. Without precise mathematical model it can determine the size of controlled variable according the rule table organized by experience. In general, fuzzy control input variables are based on system error E and error change EC, which is similar to PD control. Such control might have a good dynamic characteristic, but the static performance is not satisfactory.Combining fuzzy control and PID control, this would make a system have both flexibility-adaptablity of fuzzy control and high accuracy of PID control.Fig.2 shows the structure diagram of fuzzy-PID control system, in which fuzzy controller is responsible for selecting a different PID parameter to improve the local performance thus increasing over all performance.4 Design of fuzzy-PID controllerSpeed drive motor controller design is similar to the following example for steering gear controller design. Fuzzy controller consists of fuzzification, fuzzy-inference and defuzzification, which are based on the knowledge base.[6] Controller input error and error change, output the parameters Kp,Ki,Kd.Suppose the fuzzy set for E is{NB,NM,NS,NO,PO,PS，PM,PB}; the fuzzy set for EC、Kp、Ki and Kd is{NB,NM,NS,ZO,PS,PM,PB}. The linguistic meanings are: NB = negative big, NM = negative middle, NS = negative small, NO = negative zero, ZO = zero, PO = positive zero, PS = positive small, PM = positive middle, PB = positive big. So the membership function curves of fuzzy variables E、EC、Kp、Ki and Kd are shown in the Fig.3-Fig.7:It’s necessary to establish rule table after finishing fuzzification. According the description of rule table, 56 fuzzy conditional statements can be summed, which look like If (E is PB) and (EC is PB) then (Kp is PB) (Ki is ZO) (Kd is PB). See details in Tab.1-Tab.3.Then, the last step is defuzzification and making a lookup table. During fuzzy control, the lookup table would be embed into the program. Suppose input value is fixed, the corresponding output value would be found in the table. Actually, this would save much computing time, and the control would become simply.5 Analysis of experimental resultsExperiment used the steering gear model. The simulation circuit were shown in Fig.2. The usual PID and fuzzy PID algorithm were all simulinked in the Matlab. Responding curves obtained were shown in Fig.8 and Fig.9. The experimental result show that compared with the usual PID, the responding time of fuzzy-PID algorithm is shorter without over swing. The system dynamic performance is improved significantly.6 Conclusion and outlookThis paper provided a design scheme for controlling a smart car, which is proved practically and superlatively though experiments. Unmanned smart car is due to the development of computer technology, pattern recognition and intelligent control technique. Many countries and research gr oups are doing research in the area. But it’s a complicated system, which involves a number of technologies. So the development of each technology is important, for it would become the bottleneck of the development of smart car.Stepper motorStepper motor is the electric pulse signals into angular displacement or linear displacement of the open-loop stepper motor control element pieces. In the case ofnon-overloaded, the motor speed, stop position depends only on the pulse frequency and pulse number, regardless of load changes, when the driver receives a step pulse signal, it will drive a stepper motor to Set the direction of rotation of a fixed angle, called the "step angle", which the angle of rotation is fixed step by step operation. Number of pulses can be controlled by controlling the angular displacement, so as to achieve accurate positioning purposes; the same time by controlling the pulse frequency to control the motor rotation speed and acceleration, to achieve speed control purposes.1 WorkInduction motor is a stepper motor, does it work is the use of electronic circuits, the DC power supply into a time-sharing, multi-phase timing control current, this current stepper motor power supply, the stepper motor to work properly , The drive is sharing power supply for the stepper motor, the polyphase timing controller.Although the stepper motor has been widely used, but the stepper motor does not like a normal DC motor, AC motor in the conventional use. It must be double-ring pulse signal; power driver circuit composed of the control system can be used. Therefore, it is not easy with a good stepping motor, which involves mechanical, electrical, electronics and computers, and much other specialized knowledge.As the stepper motor actuators, electromechanical integration, one of the key products, widely used in a variety of automatic control systems. With the development of microelectronics and computer technology, increasing demand for stepper motor has applications in all areas of the national economy.2 CategoriesNow more commonly used include the reaction of step motor stepper motor (VR), permanent magnet stepper motor (PM), hybrid stepper motors (HB) and single-phase stepper motor.3 Permanent magnet stepper motorPermanent magnet stepper motor is generally two-phase, torque, and smaller, usually 7.5 degree step angle or 15 degrees;Permanent magnet stepper motor output torque, dynamic performance, but a large step angle.4 Reaction Stepper MotorReaction is generally three-phase stepping motor can achieve high torque output, step angle of 1.5 degrees is generally, but the noise and vibration are large. Reaction by the stepper motor rotor magnetic circuit made of soft magnetic materials, a number of the stator phase excitation winding, the use of permeability changes in torque.Step Motor simple structure, low production costs, step angle is small; but the dynamic performance is poor.5 Hybrid Stepping MotorHybrid Step Motor combines reactive, permanent magnet stepper motors of both, it's a small step angle, contribute a large, dynamic performance, is currently the highest performance stepper motor. It is also sometimes referred to as Permanent Magnet Induction Stepping Motor. It consists of two phases and the five-phase: the general two-phase step angle of 1.8 degrees and the general five-phase step angle 0.72 degrees. The most widely used Stepper Motor. Stepper motor drive for energy saving6 Three-phase stepper motor drive special features:◆180% low torque output, low frequency characteristics of a good run◆Maximum output frequency 600Hz, high-speed motor control◆full range of detection of protection (over voltage, under voltage, overload)instantaneous power failure restart◆acceleration, deceleration, such as dynamic change in the stall protection function toprevent◆Electrical dynamic parameters of automatic recognition function to ensure stabilityand accuracy of the system◆quick response and high-speed shutdown◆abundant and flexible input and output interface and control, versatility◆use of SMT production and three full-mount anti-paint treatment process, productstability and high◆full range of Siemens IGBT power devices using the latest, to ensure the quality ofhigh-quality7 Basic principlesUsually for the permanent magnet rotor motor, when current flows through the stator windings, the stator windings produce a magnetic field vector. The magnetic field will lead to a rotor angle of the magnetic field makes the direction of a rotor and the stator's magnetic field direction. When the stator magnetic field vector rotating at an angle. As the rotor magnetic field is also transferred from another perspective. An electrical pulse for each input, the motor turning a point forward. It is the angular displacement of the output and input the number of pulses proportional to speed and pulse frequency is proportional to. Power to change the order of winding, the motor will reverse. Therefore, the number of available control pulse, frequency and power the motor windings of each phase in order to control the stepper motor rotation.8 Induction Stepping Motor8-1 features: Induction, compared with the traditional reactive, structural reinforced with a permanent magnet rotor, in order to provide the working point of soft magnetic materials, and the stator excitation magnetic field changes only need to provide to provide the operating point of the consumption of magnetic materials energy, so the motor efficiency, current, low heat. Due to the presence of permanent magnets, the motor has a strong EMF, the damping effect of its own good, it is relatively stable during operation, low noise, low frequency vibration. Induction can be seen as somewhat low-speed synchronous motor. A four-phase motor can be used for four-phase operation, but also can be used for two-phase operation. (Must be bipolar voltage drive), while the motor is not so reactive. For example: four phase, eight-phase operation (A-AB-B-BC-C-CD-D-DA-A) can use two-phase eight-shot run. Not difficult to find the conditions for C =, D =. a two-phase motor's internal winding consistent with the four-phase motors, small power motors are generally directly connected to the second phase, the power of larger motor, in order to facilitate the use and flexible to change the dynamic characteristics of the motor, its external connections often lead to eight (four-phase), so that when used either as a four-phase motors used, can be used for two-phase motor winding in series or parallel.8-2 classification：Induction motors can be divided in phases: two-phase motor, three phase motor, four-phase motor, five-phase motor. The frame size (motor diameter)can be divided into: 42BYG (BYG the Induction Stepping motor code), 57BYG, 86BYG, 110BYG, (international standard), and like 70BYG, 90BYG, 130BYG and so are the national standards.8-3 the stepper motor phase number of static indicators of terms: very differently on the N, S the number of magnetic field excitation coil. Common m said. Beat number: complete the necessary cyclical changes in a magnetic field pulses or conducting state with n said, or that turned a pitch angle of the motor pulses needed to four-phase motor, for example, a four-phase four-shot operation mode that AB -BC-CD-DA-AB, shot eight four-phase operation mode that A-AB-B-BC-C-CD-D-DA-A. Step angle: corresponds to a pulse signal, the angular displacement of the rotor turned with θ said. θ = 360 degrees (the rotor teeth number of J * run shot), the conventional two, four-phase, the rotor teeth 50 tooth motor as an example. Four step run-time step angle θ = 360 ° / (50 * 4) = 1.8 degrees (commonly called the whole step), eight-shot running step angle θ = 360 ° / (50 * 8) = 0.9 degrees (commonly known as half step.) Location torque: the motor is not energized in the state, its locked rotor torque (as well as by the magnetic field profile of harmonics caused by mechanical error) static torque: the motor under the rated static electricity, the motor without rotation, the motor shaft locking torque. The motor torque is a measure of volume (geometry) standards, and drive voltage and drive power, etc. has nothing to do. Although the static torque is proportional to the electromagnetic magnetizing ampere turns, and fixed air gap between the rotor teeth on, but over-use of reduced air gap, increase the excitation ampere-turns to increase the static torque is not desirable, this will cause the motor heating and mechanical noise.智能小车控制中模糊-PID控制的实现摘要：本文设计了一个自动智能小车控制系统和模糊-PID控制算法。

基于PID算法的小型恒温水浴锅控制系统的设计外文翻译

PID 控制器没有学习能力，必须正确的设定。为有效的控制系统选择正确的参数被称为整定控制器。如果控制器在零偏差从稳定开始，然后进一步的变化将导致其它一些影响过程的能测量、不能测量值的变化，并且作用于偏差值上。除主过程以外，其他的对扰动有影响的过程可以用来抑制扰动或实现对目标值的改变。供给水温的变化就构成了对过程的一个扰动。理论上，控制器能用来控制可测量对象，以及可以影响偏差的输出、输入标准值的所有过程参数。控制器在工业中被用来调节温度，压力，流速，化学组成，速度以及其它任何存在可测量的对象。汽车游览控制就是一个自动化的过程控制的例子。由于它们悠久的历史，简易，良好的理论基础以及简单的设置、维护要求，PID 控制器被许多应用实践所采纳。 2.PID 控制器理论注释：这部分描述 PID 控制器理想平行或非相互作用的形式。关于其他形式，请看“其它的表达式和 PID 形式”这部分。 PID 控制是根据它的三个参数而命名的，三参数结合起来就形成控制参数。因此：
Iout：积分值 Ki:积分时间常数，协调参数 e:偏差=SP-PV ζ ：积分时间积分值加速面向设定值的过程运动并且消除残余的只与控制器发生作用的稳态偏差。然而，因为积分从过去的积累误差作出反应，引起当前的值越过设定值（跨过设定值向其它方向改变）。想了解更多的关于积分和控制器稳定度的知识，请参见关于环路调谐的部分。 2.3 微分值过程偏差的变化率通过超时错误的斜率来计算（即它第一个关于调节的微分）并增加由微分时，间常数 Kd 引起的变化的速率。对整个控制行为的微分作用的大小称为微分值 Kd。微分值计算如下：
2．译成中文：
PID 控制器
比例积分微分控制器（PID 调节器）是一个控制环，广泛地应用于工业控制系统里的反馈机制。 PID 控制器通过调节给定值与测量值之间的偏差，给出正确的调整，从而有规律地纠正控制过程。 PID 控制器算法涉及到三个部分：比例，积分，微分。比例控制是对当前偏差的反应，积分控制是基于新近错误总数的反应，而微分控制则是基于错误变化率的反应。这三种控制的结合可用来调节过程系统，例如调节阀的位置，或者加热系统的电源调节。根据具体的工艺要求，通过 PID 控制器的参数整定，从而提供调节作用。控制器的响应可以被认为是对系统偏差的响应。注意一点的是，PID 算法不一定就是系统或系统稳定性的最佳控制。一些应用可能只需要运用一到两种方法来提供适当的系统控制。这是通过把不想要的控制输出置零取得。在控制系统中存在 P,PI,PD,PID 调节器。PI 调节器很普遍，因为微分控制对测量噪音非常敏感。积分作用的缺乏可以防止系统根据控制目标而达到它的目标值。注释：由于控制理论和应用领域的差异，很多相关变量的命名约定是常用的。控制环基础一个关于控制环类似的例子就是保持水在理想温度，涉及到两个过程，冷、热水的混合。人可以凭触觉估测水的温度。基于此他们设计一个控制行为：用冷水龙头调整过程。重复这个过程，调节热水流直到温度处于期望的稳定值。感觉水温就是对过程值或变量的测量。期望得到的温度称为给定值。控制器的输出对象和过程的输入对象称为控制参数。测量值与给定值之间的差就是偏差值，太高、太低或正常。作为一个控制器，在确定温度给定值后，就可以粗略决定改变阀门位置多少，以及怎样改变偏差值。首次估计即是 PID 控制器的比例度的确定。当它几乎正确时，PID 控制器的积分作用就是起着逐渐调整温度的作用。微分作用就是根据水温变得更热、更冷，以及变化速率来决定什么时候、怎样调整那些阀门。当偏差小时而做了一个大变动，相当于一个大的调整控制器，会导致超调。如果控制器反复进行大的变动并且反复越过给定值的改变，控制环将会不稳定。输出值将在期望值或一常量周围摆动，甚至破坏系统稳定性。人不会这样做，因为我们是有智慧的控制人员，可以从历史经验中学习，但

PID控制简介中英文翻译word版

英语资料及译文About PID controlRecently automation technology is based on the concept of feedback. Elements of feedback theory consist of three parts: measurement, comparison and implementation. Measurement variables of concern, compared with expectations, with the control system to correct the error response.The theory and application of automatic control, the key is to make the correct measurement and comparison of how best to rectify the system.PID (proportional - integral - differential) controller as the first practical use of the controller more than 50 years of history, is still the most widely used industrial controller. Simple PID controller, the use of the system does not accurately model a prerequisite that they have become the most widely used controller.PID controller is the proportion of cells (P), integral unit (I) and the differential unit (D) component.Because of its wide range of uses, the use of flexible, has been serialized products, the use of only three parameters setting (Kp, Ti and Td) can be. In many cases, does not necessarily need all three modules, which can take 1-2 unit, but the proportion of the control unit is essential.First of all, PID broad range of applications. Although many industrial processes or time-varying non-linear, but can be simplified through their basic non-linear and dynamic characteristics of the system over time, so that you can control the PID.Secondly, PID parameter can tune easier. That is, PID parameters Kp, Ti and Td can be based on the dynamic characteristics of the process of setting a timely manner. If the dynamic characteristics of the process ofchange, for example, changes may be caused by the load dynamic characteristics of the system changes, PID parameters can be re-tuning.Third, PID controller in practice is to be improved continuously, the following are two examples of improvements.In factories, we always see a lot of loops are in manual, and because of the difficulty of the course so that the "automatic" mode, a smooth working. As a result of these deficiencies, the use of the industrial control system PID is always subject to product quality, safety, waste production and energy problems. PID parameter self-tuning PID parameters in order to deal with this problem setting generated. Now, the auto-tuning or self-tuning of PID controller is a business single-loop controllers and distributed control system of a standard.In some cases the system-specific design of PID controller to control very well, but they are there are still some problems to be solved: If self-tuning should be based on the model, in order to re-PID tuning parameters online to find and maintain a good process model is more difficult. When closed-loop works, the requirements in the process of inserting must have a test signal. This method will cause disturbance, so model-based PID parameter self-tuning is not too good in the industrial applications.If self-tuning control law based on the often difficult to load disturbance caused by the impact and dynamic characteristics of the process of the impact of changes in the distinction between the effects of so disturbed overshoot controller will have to create a self-adaptive unnecessary conversion. In addition, since the control law based on the maturity of the system is not the stability of analytical methods, the reliability of parameter tuning, there are many problems.Therefore, many self-tuning PID controller parameters work in the auto-tuning mode and not in the self-tuning mode. Auto-tuning is oftenused to describe the state of open-loop based on a simple process model to determine automatic calculation of PID parameters.PID in controlling nonlinear, time-varying, coupling and parameter uncertainty and structural complexity of the process, the work is not very good. The most important thing is, if the PID controller can not control the complexity of the process, regardless of how not to use transfer parameters.Despite these shortcomings, PID controller is sometimes the most simple is the best controller.At present, the level of industrial automation has become a measure of the level of modernization in all walks of life an important sign. At the same time, the development of control theory has also experienced a classical control theory, modern control theory and intelligent control theory of three stages. Classic example of intelligent control is ambiguous, such as full-automatic washing machine. Automatic control system can be divided into open-loop control systems and closed-loop control system.A control system, including controllers, sensors, transmitters, implementing agencies, input and output interfaces. Controller's output after the output interface, the implementing agencies, added to the system was charged with; control system charged with the amount, after the sensor, transmitter, through the input interface to the controller. Different control system, the sensor, transmitter, the executing agency is not the same. For example pressure sensors need to be used in pressure control system. Electric heating control system is the sensor temperature sensor. At present, PID control and PID controller or a smart controller (instrument) has a lot of products have been in practice in engineering is widely used, there are a wide range of PID controller products, major companies have developed with PID parameter self-tuning regulator function smart (intelligent regulator), which the PID controller parameters are automatically adjusted through the intelligent or self-tuning, adaptivealgorithms to achieve. PID control are achieved using pressure, temperature, flow, liquid level controller, PID control functions to achieve the programmable logic controller (PLC), also enables the PC system, PID control and so on. Programmable Logic Controller (PLC) is the use of its closed-loop control module to achieve PID control, and programmable logic controller (PLC) can be connected directly with the ControlNet, such as Rockwell's PLC-5 and so on. There can be the controller PID control functions, such as Rockwell's Logix product line, it can be connected directly with the ControlNet, using the Internet to achieve its long-range control functions.1, Open-loop control systemOpen-loop control system is the object of the output (volume control) on the controller does not affect the output. In this control system, do not rely on volume will be charged back to the formation of anti-any closed-loop circuit.2, Closed-loop control systemClosed-loop control system is characterized by the output of the system object (volume control) will be sent back to the impact of anti-output controller to form one or more of the closed-loop. Closed-loop control system has positive feedback and negative feedback, if the feedback signal and the system to set the value of the signal the other hand, is referred to as negative feedback, if the same polarity is called positive feedback, the general closed-loop control system using negative feedback, also known as negative feedback control system. Closed-loop control system has many examples. For example, people with negative feedback is a closed-loop control system, the eye is the sensor to act as a feedback, the human body system through the constant variety of the right to make amendments to the final action. If there are no eyes, there is no feedback loops, it became an open-loop control system. Another example, when a full-automatic washing machine with a realcontinuously check whether the washed clothing, and wash off automatically after the power supply, it is a closed-loop control system.3, Step responseStep response refers to a step input (step function) when added to the system, the system output. Steady-state error is the system response into the steady-state, the system's desired output and actual output of the difference. The performance of control system can be stable, accurate and fast three words to describe. Stability is the stability of the system, a system must be able to work, first of all must be stable, from the step response should be a convergence point of view; quasi-control system refers to the accuracy, control precision, stability is usually state error description, it said the system output steady-state value and the difference between expectations; fast control system refers to the rapid response, and usually to a quantitative description of the rise time.4, Theory and the characteristics of PID controlIn engineering practice, the most widely used control laws regulate the proportional, integral, differential control, referred to as PID control, also known as PID regulator. PID controller has been available for nearly 70 years of history, which in its simple structure, stable, reliable, easy to adjust and become the main industrial control technologies. When charged with the structure and parameters of the object can not completely grasp, or lack of accurate mathematical model, control theory it is difficult using other techniques, the system controller structure and parameters have to rely on experience and on-site testing to determine when the application PID control of the most convenient technology. That is, when we do not fully understand the system and charged with an object, or can not be an effective means of measuring system parameters to obtain the most suitable PID control technology. PID control, in practice there are PI and PD control. PID controller is the error of the system, using proportional, integral, differential calculationfor the control of the volume control.The ratio of (P) controlProportional control is one of the most simple control methods. The controller's output and input error signal proportional to the relationship. The output has the existence of steady-state error when there is only a proportional control system.Integral (I) controlIn integral control, the controller's output and input error signal is proportional to the integral relationship. For an automatic control system, if steady-state error exists after entering the steady-state, the control system is referred to as steady-state error or having a poor system. In order to eliminate steady-state error, the controller must be the introduction of the "key points." Points of error depend on the time of the points of the increase over time, will increase the integral term. In this way, even if the error is very small, integral term will increase as time increases, it increased to promote the output of the controller so that steady-state error further reduced until zero. Therefore, the proportional + integral (PI) controller, you can make the system after entering the steady-state non-steady-state error.Differential (D) controlIn the differential control, the controller's output and input of the differential error signal (the rate of change of error) is directly proportional to the relationship. Automatic control system to overcome the errors in the adjustment process may be unstable or even oscillation. The reason is because of greater inertial components (links) or there is lag components, can inhibit the role of error, the changes always lag behind changes in error. The solution is to inhibit the changes in the role of error "in advance", that is close to zero in the error and suppress the role of error should be zero. This means that the controller only the introduction of the "proportion" of often is not enough, the proportion of the role isonly to enlarge the amplitude error, the current need to increase the "differential item" that can change the trend of prediction error, In this way, with the proportion of + differential controller, will be able to advance so that the role of inhibitory control error equal to zero or even negative, thus avoiding the amount charged with a serious overshoot. Therefore, greater inertia of the charged object or lag, the proportion of + differential (PD) controller to improve the system in the regulation of the dynamic characteristics of the process.PID控制简介当今的自动控制技术都是基于反馈的概念。

PID控制简介中英文翻译word版

英语资料及译文About PID controlRecently automation technology is based on the concept of feedback. Elements of feedback theory consist of three parts: measurement, comparison and implementation. Measurement variables of concern, compared with expectations, with the control system to correct the error response.The theory and application of automatic control, the key is to make the correct measurement and comparison of how best to rectify the system.PID (proportional - integral - differential) controller as the first practical use of the controller more than 50 years of history, is still the most widely used industrial controller. Simple PID controller, the use of the system does not accurately model a prerequisite that they have become the most widely used controller.PID controller is the proportion of cells (P), integral unit (I) and the differential unit (D) component.Because of its wide range of uses, the use of flexible, has been serialized products, the use of only three parameters setting (Kp, Ti and Td) can be. In many cases, does not necessarily need all three modules, which can take 1-2 unit, but the proportion of the control unit is essential.First of all, PID broad range of applications. Although many industrial processes or time-varying non-linear, but can be simplified through their basic non-linear and dynamic characteristics of the system over time, so that you can control the PID.Secondly, PID parameter can tune easier. That is, PID parameters Kp, Ti and Td can be based on the dynamic characteristics of the process of setting a timely manner. If the dynamic characteristics of the process ofchange, for example, changes may be caused by the load dynamic characteristics of the system changes, PID parameters can be re-tuning.Third, PID controller in practice is to be improved continuously, the following are two examples of improvements.In factories, we always see a lot of loops are in manual, and because of the difficulty of the course so that the "automatic" mode, a smooth working. As a result of these deficiencies, the use of the industrial control system PID is always subject to product quality, safety, waste production and energy problems. PID parameter self-tuning PID parameters in order to deal with this problem setting generated. Now, the auto-tuning or self-tuning of PID controller is a business single-loop controllers and distributed control system of a standard.In some cases the system-specific design of PID controller to control very well, but they are there are still some problems to be solved: If self-tuning should be based on the model, in order to re-PID tuning parameters online to find and maintain a good process model is more difficult. When closed-loop works, the requirements in the process of inserting must have a test signal. This method will cause disturbance, so model-based PID parameter self-tuning is not too good in the industrial applications.If self-tuning control law based on the often difficult to load disturbance caused by the impact and dynamic characteristics of the process of the impact of changes in the distinction between the effects of so disturbed overshoot controller will have to create a self-adaptive unnecessary conversion. In addition, since the control law based on the maturity of the system is not the stability of analytical methods, the reliability of parameter tuning, there are many problems.Therefore, many self-tuning PID controller parameters work in the auto-tuning mode and not in the self-tuning mode. Auto-tuning is oftenused to describe the state of open-loop based on a simple process model to determine automatic calculation of PID parameters.PID in controlling nonlinear, time-varying, coupling and parameter uncertainty and structural complexity of the process, the work is not very good. The most important thing is, if the PID controller can not control the complexity of the process, regardless of how not to use transfer parameters.Despite these shortcomings, PID controller is sometimes the most simple is the best controller.At present, the level of industrial automation has become a measure of the level of modernization in all walks of life an important sign. At the same time, the development of control theory has also experienced a classical control theory, modern control theory and intelligent control theory of three stages. Classic example of intelligent control is ambiguous, such as full-automatic washing machine. Automatic control system can be divided into open-loop control systems and closed-loop control system.A control system, including controllers, sensors, transmitters, implementing agencies, input and output interfaces. Controller's output after the output interface, the implementing agencies, added to the system was charged with; control system charged with the amount, after the sensor, transmitter, through the input interface to the controller. Different control system, the sensor, transmitter, the executing agency is not the same. For example pressure sensors need to be used in pressure control system. Electric heating control system is the sensor temperature sensor. At present, PID control and PID controller or a smart controller (instrument) has a lot of products have been in practice in engineering is widely used, there are a wide range of PID controller products, major companies have developed with PID parameter self-tuning regulator function smart (intelligent regulator), which the PID controller parameters are automatically adjusted through the intelligent or self-tuning, adaptivealgorithms to achieve. PID control are achieved using pressure, temperature, flow, liquid level controller, PID control functions to achieve the programmable logic controller (PLC), also enables the PC system, PID control and so on. Programmable Logic Controller (PLC) is the use of its closed-loop control module to achieve PID control, and programmable logic controller (PLC) can be connected directly with the ControlNet, such as Rockwell's PLC-5 and so on. There can be the controller PID control functions, such as Rockwell's Logix product line, it can be connected directly with the ControlNet, using the Internet to achieve its long-range control functions.1, Open-loop control systemOpen-loop control system is the object of the output (volume control) on the controller does not affect the output. In this control system, do not rely on volume will be charged back to the formation of anti-any closed-loop circuit.2, Closed-loop control systemClosed-loop control system is characterized by the output of the system object (volume control) will be sent back to the impact of anti-output controller to form one or more of the closed-loop. Closed-loop control system has positive feedback and negative feedback, if the feedback signal and the system to set the value of the signal the other hand, is referred to as negative feedback, if the same polarity is called positive feedback, the general closed-loop control system using negative feedback, also known as negative feedback control system. Closed-loop control system has many examples. For example, people with negative feedback is a closed-loop control system, the eye is the sensor to act as a feedback, the human body system through the constant variety of the right to make amendments to the final action. If there are no eyes, there is no feedback loops, it became an open-loop control system. Another example, when a full-automatic washing machine with a realcontinuously check whether the washed clothing, and wash off automatically after the power supply, it is a closed-loop control system.3, Step responseStep response refers to a step input (step function) when added to the system, the system output. Steady-state error is the system response into the steady-state, the system's desired output and actual output of the difference. The performance of control system can be stable, accurate and fast three words to describe. Stability is the stability of the system, a system must be able to work, first of all must be stable, from the step response should be a convergence point of view; quasi-control system refers to the accuracy, control precision, stability is usually state error description, it said the system output steady-state value and the difference between expectations; fast control system refers to the rapid response, and usually to a quantitative description of the rise time.4, Theory and the characteristics of PID controlIn engineering practice, the most widely used control laws regulate the proportional, integral, differential control, referred to as PID control, also known as PID regulator. PID controller has been available for nearly 70 years of history, which in its simple structure, stable, reliable, easy to adjust and become the main industrial control technologies. When charged with the structure and parameters of the object can not completely grasp, or lack of accurate mathematical model, control theory it is difficult using other techniques, the system controller structure and parameters have to rely on experience and on-site testing to determine when the application PID control of the most convenient technology. That is, when we do not fully understand the system and charged with an object, or can not be an effective means of measuring system parameters to obtain the most suitable PID control technology. PID control, in practice there are PI and PD control. PID controller is the error of the system, using proportional, integral, differential calculationfor the control of the volume control.The ratio of (P) controlProportional control is one of the most simple control methods. The controller's output and input error signal proportional to the relationship. The output has the existence of steady-state error when there is only a proportional control system.Integral (I) controlIn integral control, the controller's output and input error signal is proportional to the integral relationship. For an automatic control system, if steady-state error exists after entering the steady-state, the control system is referred to as steady-state error or having a poor system. In order to eliminate steady-state error, the controller must be the introduction of the "key points." Points of error depend on the time of the points of the increase over time, will increase the integral term. In this way, even if the error is very small, integral term will increase as time increases, it increased to promote the output of the controller so that steady-state error further reduced until zero. Therefore, the proportional + integral (PI) controller, you can make the system after entering the steady-state non-steady-state error.Differential (D) controlIn the differential control, the controller's output and input of the differential error signal (the rate of change of error) is directly proportional to the relationship. Automatic control system to overcome the errors in the adjustment process may be unstable or even oscillation. The reason is because of greater inertial components (links) or there is lag components, can inhibit the role of error, the changes always lag behind changes in error. The solution is to inhibit the changes in the role of error "in advance", that is close to zero in the error and suppress the role of error should be zero. This means that the controller only the introduction of the "proportion" of often is not enough, the proportion of the role isonly to enlarge the amplitude error, the current need to increase the "differential item" that can change the trend of prediction error, In this way, with the proportion of + differential controller, will be able to advance so that the role of inhibitory control error equal to zero or even negative, thus avoiding the amount charged with a serious overshoot. Therefore, greater inertia of the charged object or lag, the proportion of + differential (PD) controller to improve the system in the regulation of the dynamic characteristics of the process.PID控制简介当今的自动控制技术都是基于反馈的概念。

pid控制外文加中文文献(适用于毕业论文外文翻译+中英文对照)

PID controllerFrom Wikipedia, the free encyclopediaA proportional–integral–derivative controller (PID controller) is a generic .control loop feedback mechanism widely used in industrial control systems.A PID controller attempts to correct the error between a measured process variable and a desired setpoint by calculating and then outputting a corrective action that can adjust the process accordingly.The PID controller calculation (algorithm) involves three separate parameters; the Proportional, the Integral and Derivative values. The Proportional value determines the reaction to the current error, the Integral determines the reaction based on the sum of recent errors and the Derivative determines the reaction to the rate at which the error has been changing. The weightedsum of these three actions is used to adjust the process via a control element such as the position of a control valve or the power supply of a heating element.By "tuning" the three constants in the PID controller algorithm the PID can provide control action designed for specific process requirements. The response of the controller can be described in terms of the responsiveness of the controller to an error, the degree to which the controller overshoots the setpoint and the degree of system oscillation. Note that the use of the PID algorithm for control does not guarantee optimal control of the system or system stability.Some applications may require using only one or two modes to provide the appropriate system control. This is achieved by setting the gain of undesired control outputs to zero. A PID controller will be called a PI, PD, P or I controller in the absence of the respective control actions. PI controllers are particularly common, since derivative action is very sensitive to measurement noise, and the absence of an integral value may prevent the system from reaching its target value due to the control action.A block diagram of a PID controllerNote: Due to the diversity of the field of control theory and application, many naming conventions for the relevant variables are in common use.1.Control loop basicsA familiar example of a control loop is the action taken to keep one's shower water at the ideal temperature, which typically involves the mixing of two process streams, cold and hot water. The person feels the water to estimate its temperature. Based on this measurement they perform a control action: use the cold water tap to adjust the process. The person would repeat this input-output control loop, adjusting the hot water flow until the process temperature stabilized at the desired value.Feeling the water temperature is taking a measurement of the process value or process variable (PV). The desired temperature is called the setpoint (SP). The output from the controller and input to the process (the tap position) is called the manipulated variable (MV). The difference between the measurement and the setpoint is the error (e), too hot or too cold and by how much.As a controller, one decides roughly how much to change the tap position (MV) after one determines the temperature (PV), and therefore the error. This first estimate is the equivalent of the proportional action of a PID controller. The integral action of a PID controller can be thought of as gradually adjusting the temperature when it is almost right. Derivative action can be thought of as noticing the water temperature is getting hotter or colder, and how fast, and taking that into account when deciding how to adjust the tap.Making a change that is too large when the error is small is equivalent to a high gain controller and will lead toovershoot. If the controller were to repeatedly make changes that were too large and repeatedly overshoot the target, this control loop would be termed unstable and the output would oscillate around the setpoint in either a constant, growing, or decaying sinusoid. A human would not do this because we are adaptive controllers, learning from the process history, but PID controllers do not have the ability to learn and must be set up correctly. Selecting the correct gains for effective control is known as tuning the controller.If a controller starts from a stable state at zero error (PV = SP), then further changes by the controller will be in response to changes in other measured or unmeasured inputs to the process that impact on the process, and hence on the PV. Variables that impact on the process other than the MV are known as disturbances and generally controllers are used to reject disturbances and/or implement setpoint changes. Changes in feed water temperature constitute a disturbance to the shower process.In theory, a controller can be used to control any process which has a measurable output (PV), a known ideal value for that output (SP) and an input to the process (MV) that will affect the relevant PV. Controllers are used in industry to regulate temperature, pressure, flow rate, chemical composition, speed and practically every other variable for which a measurement exists. Automobile cruise control is an example of a process which utilizes automated control.Due to their long history, simplicity, well grounded theory and simple setup and maintenance requirements, PID controllers are the controllers of choice for many of these applications.2.PID controller theoryNote: This section describes the ideal parallel or non-interacting form of the PID controller. For other forms please see the Section "Alternative notation and PID forms".The PID control scheme is named after its three correcting terms, whose sum constitutes the manipulated variable (MV). Hence:where Pout, Iout, and Dout are the contributions to the output from the PID controller from each of the three terms, as defined below.2.1. Proportional termThe proportional term makes a change to the output that is proportional to the current error value. The proportional response can be adjusted by multiplying the error by a constant Kp, called the proportional gain.The proportional term is given by:WherePout: Proportional outputKp: Proportional Gain, a tuning parametere: Error = SP − PVt: Time or instantaneous time (the present)Change of response for varying KpA high proportional gain results in a large change in the output for a given change in the error. If the proportional gain is too high, the system can become unstable (See the section on Loop Tuning). In contrast, a small gain results in a small output response to a large input error, and a less responsive (or sensitive) controller. If the proportional gain is too low, the control action may be too small when responding to system disturbances.In the absence of disturbances, pure proportional control will not settle at its target value, but will retain a steady state error that is a function of the proportional gain and the process gain. Despite the steady-state offset, both tuning theory and industrial practice indicate that it is the proportional term that should contribute the bulk of the output change.2.2.Integral termThe contribution from the integral term is proportional to both the magnitude of the error and the duration of the error. Summing the instantaneous error over time (integrating the error) gives the accumulated offset that should have been corrected previously. The accumulated error is then multiplied by the integral gain and added to the controller output. The magnitude of the contribution of the integral term to the overall control action is determined by the integral gain, Ki.The integral term is given by:Change of response for varying KiWhereIout: Integral outputKi: Integral Gain, a tuning parametere: Error = SP − PVτ: Time in the past contributing to the integral responseThe integral term (when added to the proportional term) accelerates themovement of the process towards setpoint and eliminates the residual steady-state error that occurs with a proportional only controller. However, since the integral term is responding to accumulated errors from the past, it can cause the present value to overshoot the setpoint value (cross over the setpoint and then create a deviation in the other direction). For further notes regarding integral gain tuning and controller stability, see the section on loop tuning.2.3 Derivative termThe rate of change of the process error is calculated by determining the slope of the error over time (i.e. its first derivative with respect to time) and multiplying this rate of change by the derivative gain Kd. The magnitude of the contribution of the derivative term to the overall control action is termed the derivative gain, Kd.The derivative term is given by:Change of response for varying KdWhereDout: Derivative outputKd: Derivative Gain, a tuning parametere: Error = SP − PVt: Time or instantaneous time (the present)The derivative term slows the rate of change of the controller output and this effect is most noticeable close to the controller setpoint. Hence, derivative control isused to reduce the magnitude of the overshoot produced by the integral component and improve the combined controller-process stability. However, differentiation of a signal amplifies noise and thus this term in the controller is highly sensitive to noise in the error term, and can cause a process to become unstable if the noise and the derivative gain are sufficiently large.2.4 SummaryThe output from the three terms, the proportional, the integral and the derivative terms are summed to calculate the output of the PID controller. Defining u(t) as the controller output, the final form of the PID algorithm is:and the tuning parameters areKp: Proportional Gain - Larger Kp typically means faster response since thelarger the error, the larger the Proportional term compensation. An excessively large proportional gain will lead to process instability and oscillation.Ki: Integral Gain - Larger Ki implies steady state errors are eliminated quicker. The trade-off is larger overshoot: any negative error integrated during transient response must be integrated away by positive error before we reach steady state.Kd: Derivative Gain - Larger Kd decreases overshoot, but slows down transient response and may lead to instability due to signal noise amplification in the differentiation of the error.3. Loop tuningIf the PID controller parameters (the gains of the proportional, integral and derivative terms) are chosen incorrectly, the controlled process input can be unstable, i.e. its output diverges, with or without oscillation, and is limited only by saturation or mechanical breakage. Tuning a control loop is the adjustment of its control parameters (gain/proportional band, integral gain/reset, derivative gain/rate) to the optimum values for the desired control response.The optimum behavior on a process change or setpoint change varies depending on the application. Some processes must not allow an overshoot of the processvariable beyond the setpoint if, for example, this would be unsafe. Other processes must minimize the energy expended in reaching a new setpoint. Generally, stability of response (the reverse of instability) is required and the process must not oscillate for any combination of process conditions and setpoints. Some processes have a degree of non-linearity and so parameters that work well at full-load conditions don't work when the process is starting up from no-load. This section describes some traditional manual methods for loop tuning.There are several methods for tuning a PID loop. The most effective methods generally involve the development of some form of process model, then choosing P, I, and D based on the dynamic model parameters. Manual tuning methods can be relatively inefficient.The choice of method will depend largely on whether or not the loop can be taken "offline" for tuning, and the response time of the system. If the system can be taken offline, the best tuning method often involves subjecting the system to a step change in input, measuring the output as a function of time, and using this response to determine the control parameters.Choosing a Tuning MethodMethodAdvantagesDisadvantagesManual TuningNo math required. Online method.Requires experiencedpersonnel.Ziegler–NicholsProven Method. Online method.Process upset, sometrial-and-error, very aggressive tuning.Software ToolsConsistent tuning. Online or offline method. May includevalve and sensor analysis. Allow simulation before downloading.Some costand training involved.Cohen-CoonGood process models.Some math. Offline method. Only good for first-order processes.3.1 Manual tuningIf the system must remain online, one tuning method is to first set the I and D values to zero. Increase the P until the output of the loop oscillates, then the P shouldbe left set to be approximately half of that value for a "quarter amplitude decay" type response. Then increase D until any offset is correct in sufficient time for the process. However, too much D will cause instability. Finally, increase I, if required, until the loop is acceptably quick to reach its reference after a load disturbance. However, too much I will cause excessive response and overshoot. A fast PID loop tuning usually overshoots slightly to reach the setpoint more quickly; however, some systems cannot accept overshoot, in which case an "over-damped" closed-loop system is required, which will require a P setting significantly less than half that of the P setting causing oscillation.3.2Ziegler –Nichols methodAnother tuning method is formally known as the Ziegler –Nichols method, introduced by John G . Ziegler and Nathaniel B. Nichols. As in the method above, the I and D gains are first set to zero. The "P" gain is increased until it reaches the "critical gain" Kc at which the output of theloop starts to oscillate. Kc and the oscillation period Pc are used to set the gains as shown:3.3 PID tuning softwareMost modern industrial facilities no longer tune loops using the manualcalculation methods shown above. Instead, PID tuning and loop optimization software are used to ensure consistent results. These software packages will gather the data, develop process models, and suggest optimal tuning. Some software packages can even develop tuning by gathering data from reference changes.Mathematical PID loop tuning induces an impulse in the system, and then uses the controlled system's frequency response to design the PID loop values. In loops with response times of several minutes, mathematical loop tuning is recommended, because trial and error can literally take days just to find a stable set of loop values. Optimal values are harder to find. Some digital loop controllers offer a self-tuning feature in which very small setpoint changes are sent to the process, allowing the controller itself to calculate optimal tuning values.Other formulas are available to tune the loop according to different performance criteria.4 Modifications to the PID algorithmThe basic PID algorithm presents some challenges in control applications that have been addressed by minor modifications to the PID form.One common problem resulting from the ideal PID implementations is integralwindup. This can be addressed by:Initializing the controller integral to a desired valueDisabling the integral function until the PV has entered the controllable region Limiting the time period over which the integral error is calculatedPreventing the integral term from accumulating above or below pre-determined boundsMany PID loops control a mechanical device (for example, a valve). Mechanical maintenance can be a major cost and wear leads to control degradation in the form of either stiction or a deadband in the mechanical response to an input signal. The rate of mechanical wear is mainly a function of how often a device is activated to make a change. Where wear is a significant concern, the PID loop may have an output deadband to reduce the frequency of activation of the output (valve). This is accomplished by modifying the controller to hold its output steady if the changewould be small (within the defined deadband range). The calculated output must leave the deadband before the actual output will change.The proportional and derivative terms can produce excessive movement in the output when a system is subjected to an instantaneous "step" increase in the error, such as a large setpoint change. In the case of the derivative term, this is due to taking the derivative of the error, which is very large in the case of an instantaneous step change.5. Limitations of PID controlWhile PID controllers are applicable to many control problems, they can perform poorly in some applications.PID controllers, when used alone, can give poor performance when the PID loop gains must be reduced so that the control system does not overshoot, oscillate or "hunt" about the control setpoint value. The control system performance can be improved by combining the feedback (or closed-loop) control of a PID controller with feed-forward (or open-loop) control. Knowledge about the system (such as the desired acceleration and inertia) can be "fed forward" and combined with the PID output to improve the overall system performance. The feed-forward value alone can often provide the major portion of the controller output. The PID controller can then be used primarily to respond to whatever difference or "error" remains between the setpoint (SP) and the actual value of the process variable (PV). Since the feed-forward output is not affected by the process feedback, it can never cause the control system to oscillate, thus improving the system response and stability.For example, in most motion control systems, in order to accelerate a mechanical load under control, more force or torque is required from the prime mover, motor, or actuator. If a velocity loop PID controller is being used to control the speed of the load and command the force or torque being applied by the prime mover, then it is beneficial to take the instantaneous acceleration desired for the load, scale that value appropriately and add it to the output of the PID velocity loop controller. This means that whenever the load is being accelerated or decelerated, a proportional amount of force is commanded from the prime mover regardless of the feedback value. The PID loop in this situation uses the feedback information to effect any increase or decrease of the combined output in order to reduce the remaining difference between theprocess setpoint and thefeedback value. Working together, the combined open-loop feed-forward controller and closed-loop PID controller can provide a more responsive, stable and reliable control system.Another problem faced with PID controllers is that they are linear. Thus, performance of PID controllers in non-linear systems (such as HV AC systems) is variable. Often PID controllers are enhanced through methods such as PID gain scheduling or fuzzy logic. Further practical application issues can arise from instrumentation connected to the controller. A high enough sampling rate, measurement precision, and measurement accuracy are required to achieve adequate control performance.A problem with the Derivative term is that small amounts of measurement or process noise can cause large amounts of change in the output. It is often helpful to filter the measurements with a low-pass filter in order to remove higher-frequency noise components. However, low-pass filtering and derivative control can cancel each other out, so reducing noise by instrumentation means is a much better choice. Alternatively, the differential band can be turned off in many systems with little loss of control. This is equivalent to using the PID controller as a PI controller.6. Cascade controlOne distinctive advantage of PID controllers is that two PID controllers can be used together to yield better dynamic performance. This is called cascaded PID control. In cascade control there are two PIDs arranged with one PID controlling the set point of another. A PID controller acts as outer loop controller, which controls the primary physical parameter, such as fluid level or velocity. The other controller acts as inner loop controller, which reads the output of outer loop controller as set point, usually controlling a more rapid changing parameter, flowrate or accelleration. It can be mathematically proved that the working frequency of the controller is increased and the time constant of the object is reduced by using cascaded PID controller.[vague]7. Physical implementation of PID controlIn the early history of automatic process control the PID controller was implemented as a mechanical device. These mechanical controllers used a lever, spring and a mass and were often energized by compressed air. These pneumatic controllers were once the industry standard.Electronic analog controllers can be made from a solid-state or tube amplifier, a capacitor and a resistance. Electronic analog PID control loops were often found within more complex electronic systems, for example, the head positioning of a disk drive, the power conditioning of a power supply, or even the movement-detection circuit of a modern seismometer. Nowadays, electronic controllers have largely been replaced by digital controllers implemented with microcontrollers or FPGAs.Most modern PID controllers in industry are implemented in software in programmable logic controllers (PLCs) or as a panel-mounted digital controller. Software implementations have the advantages that they are relatively cheap and are flexible with respect to the implementation of the PID algorithm.8.Alternative nomenclature and PID forms8.1 PseudocodeHere is a simple software loop that implements the PID algorithm:8.2 Ideal versus standard PID formThe form of the PID controller most often encountered in industry, and the one most relevant to tuning algorithms is the "standard form". In this form the Kp gain is applied to the Iout, and Dout terms, yielding:WhereTi is the Integral TimeTd is the Derivative TimeIn the ideal parallel form, shown in the Controller Theory sectionthe gain parameters are related to the parameters of the standard formthroughand Kd = KpTd. This parallel form, where the parameters are treated as simple gains, is the most general and flexible form. However, it is also the form where the parameters have the least physical interpretation and is generally reserved for theoretical treatment of the PID controller. The "standard" form, despite being slightly more complex mathematically, is more common in industry.8.3Laplace form of the PID controllerSometimes it is useful to write the PID regulator in Laplace transform form:Having the PID controller written in Laplace form and having the transfer function of the controlled system, makes it easy to determine the closed-loop transfer function of the system.8.4Series / interacting formAnother representation of the PID controller is the series, or "interacting" form. This form essentially consists of a PD and PI controller in series, and it made early (analog) controllers easier to build. When the controllers later became digital, many kept using the interacting form.[edit] ReferencesLiptak, Bela (1995). Instrument Engineers' Handbook: Process Control. Radnor, Pennsylvania: Chilton Book Company, 20-29. ISBN 0-8019-8242-1.Van, Doren, Vance J. (July 1, 2003). "Loop Tuning Fundamentals". Control Engineering. Red Business Information.Sellers, David. An Overview of Proportional plus Integral plus Derivative Control and Suggestions for Its Successful Application and Implementation (PDF). Retrieved on 2007-05-05.Articles, Whitepapers, and tutorials on PID controlGraham, Ron (10/03/2005). FAQ on PID controller tuning. Retrieved on2007-05-05.PID控制器比例积分微分控制器（PID调节器）是一个控制环，广泛地应用于工业控制系统里的反馈机制。

PID控制中英文对照翻译

外文资料与翻译PID Contro l6.1 IntroductionThe PID controller is the most common form of feedback. It was an essential element of early governors and it became the standard tool when process control emerged in the 1940s. In process control today, more than 95% of the control loops are of PID type, most loops are actually PI control. PID controllers are today found in all areas where control is used. The controllers come in many different forms. There are standalone systems in boxes for one or a few loops, which are manufactured by the hundred thousands yearly. PID control is an important ingredient of a distributed control system. The controllers are also embedded in many special purpose control systems. PID control is often combined with logic, sequential functions, selectors, and simple function blocks to build the complicated automation systems used for energy production, transportation, and manufacturing. Many sophisticated control strategies, such as model predictive control, are also organized hierarchically. PID control is used at the lowest level; the multivariable controller gives the set points to the controllers at the lower level. The PID controller can thus be said to be the “bread and butter of control engineering. It is an important component in every control engineer’s tool box.PID controllers have survived many changes in technology, from mechanics and pneumatics to microprocessors via electronic tubes, transistors, integrated circuits. The microprocessor has had a dramatic influence the PID controller. Practically all PID controllers made today are based on microprocessors. This has given opportunities to provide additional features like automatic tuning, gain scheduling, and continuous adaptation.6.2 AlgorithmWe will start by summarizing the key features of the PID controller. The “textbook” version of the PID algorithm is described by:()()()()⎪⎪⎭⎫ ⎝⎛++=⎰dt t de d e t e K t u T T d t i 01ττ 6.1 where y is the measured process variable, r the reference variable, u is the control signal and e is the control error （e =sp y − y ）. The reference variable is often calledthe set point. The control signal is thus a sum of three terms: the P-term （which is proportional to the error）, the I-term （which is proportional to the integral of the error）, and the D-term （which is proportional to the derivative of the error）. The controller parameters are proportional gain K, integral time T i, and derivative time T d. The integral, proportional and derivative part can be interpreted as control actions based on the past, the present and the future as is illustrated in Figure 2.2. The derivative part can also be interpreted as prediction by linear extrapolation as is illustrated in Figure 2.2. The action of the different terms can be illustrated by the following figures which show the response to step changes in the reference value in a typical case.Effects of Proportional, Integral and Derivative ActionProportional control is illustrated in Figure 6.1. The controller is given by D6.1E with T i= and T d=0. The figure shows that there is always a steady state error in proportional control. The error will decrease with increasing gain, but the tendency towards oscillation will also increase.Figure 6.2 illustrates the effects of adding integral. It follows from D6.1E that the strength of integral action increases with decreasing integral time T i. The figure shows that the steady state error disappears when integral action is used. Compare with the discussion of the “magic of integral action” in Section 2.2. The tendency for oscillation also increases with decreasing T i. The properties of derivative action are illustrated in Figure 6.3.Figure 6.3 illustrates the effects of adding derivative action. The parameters K and T i are chosen so that the closed loop system is oscillatory. Damping increases with increasing derivative time, but decreases again when derivative time becomes too large. Recall that derivative action can be interpreted as providing prediction by linear extrapolation over the time T d. Using this interpretation it is easy to understand that derivative action does not help if the prediction time T d is too large. In Figure 6.3 the period of oscillation is about 6 s for the system without derivative Chapter 6. PID ControlFigure 6.1Figure 6.2Derivative actions cease to be effective when T d is larger than a 1 s (one sixth of the period). Also notice that the period of oscillation increases when derivative time is increased.A PerspectiveThere is much more to PID than is revealed by （6.1）. A faithful implementation of the equation will actually not result in a good controller. To obtain a good PID controller it is also necessary to consider。

PID控制器中英文对照外文翻译文献

中英文对照外文翻译文献(文档含英文原文和中文翻译)外文:Memory-Based On-Line Tuning of PID Controllers for Nonlinear Systems Abstract—Since most processes have nonlinearities, controller design schemes to deal with such systems are required.On the other hand, PID controllers have been widely used for process systems. Therefore, in this paper, a new design scheme of PID controllers based on a memory-based(MB) modeling is proposed for nonlinear systems. According to the MB modeling method, some local models are automatically generated based on input/output data pairs of the controlled object stored in the data-base. The proposed scheme generates PID parameters using stored input/output data in the data-base. This scheme can adjust the PID parameters in an on-line manner even if the system has nonlinear properties. Finally, the effectiveness of the newly proposed control scheme is numerically evaluated on a simulation example.I. INTRODUCTIONIn recent years, many complicated control algorithms such as adaptive control theory or robust control theory have been proposed and implemented. However, in industrial processes, PID controllers[1], [2], [3] have been widely employed for about 80% or more of control loops. The reasons are summarized as follows. (1) the control structure is quitsimple; (2) the physical meaning of control parameters is clear; and (3) the operators’ know-how can be easily utilized in designing controllers. Therefore, itis still attractive todesign PID controllers. However, since most process systems have nonlinearities, it is difficult to obtain good control performances for such systems simply using the fixed PIDparameters. Therefore, PID parameters tuning methods using neural networks(NN)[4] and genetic algorithms(GA)[5] have been proposed until now. According to these methods, the learning cost is considerably large, and these PID parameters cannot be adequately adjusted due to the nonlinear properties. Therefore, it is quite difficult to obtain good control performances using these conventional schemes.By the way, development of computers enables us to memorize, fast retrieve and read out a large number of data. By these advantages, the following method has been proposed: Whenever new data is obtained, the data is stored.Next, similar neighbors to the informat ion requests, called’queries’, are selected from the stored data. Furthermore,the local model is constructed using these neighbors. Thismemory-based(MB) modeling method, is called Just-In-Time(JIT) method[6], [7] , Lazy Learning method[8] or Model-on-Demand(MoD)[9], and these scheme have lots of attention in last decade.In this paper, a design scheme of PID controllers based onthe MB modeling method is discussed. A few PID controllers have been already proposed based on the JIT method[10] and the MoD method[11] which belong to the MB modeling methods. According to the former method, the JIT method is used as the purpose of supplementing the feedback controller with a PID structure. However, the tracking property is not guaranteed enough due to the nonlinearities in the case where reference signals are changed, because the controller does not includes any integral action in the whole control system. On the other hand, the latter method has a PID control structure.PID parameters are tuned by operators’ skills, and they are stored in the data-base in advance. And also, a suitable set of PID parameters is generated using the stored data. However,the good control performance cannot be necessarily obtained in the case where nonlinearities are included in the controlled object and/or system parameters are changed, because PID parameters are not tuned in an on-line manner corresponding to characteristics of the controlled object. Therefore, in this paper, a design scheme of PID controllers based on the MB modeling method is newly proposed.According to the proposed method, PID parameterswhich are obtained using the MB modeling method areadequately tuned in proportion to control errors, and modifiedPID parameters are stored in the data-base. Therefore, moresuitable PID parameters corresponding to characteristics ofthe controlled object are newly stored. Moreover, an algorithmto avoid the excessive increase of the stored data,is further discussed. This algorithm yields the reduction of memories and computational costs. Finally, the effectiveness of the newly proposed control scheme is examined on asimulation example.II. PID CONTROLLER DESIGN BASED ON MEMORY-BASED MODELING METHODA. MB modeling methodFirst, the following discrete-time nonlinear system is considered:, （1）where y(t) denotes the system output and f(·) denotes the nonlinear function. Moreover, _(t−1) is called ’information vector’, which is defied by the following equation:)](),1(),(,),1([:)(u y n t u t u n t y t y t ----= φ, （2） where u(t) denotes the system input. Also, ny and nure spectively denote the orders of the system output and the system input, respectively. According to the MB modeling method, the data is stored in the form of the information vector _ expressed in Eq.(2). Moreover, _(t) is required in calculating the estimate of the output y(t+1) called ’query’.That is, after some similar neighbors to the query are selected from the data-base, the predictive value of the system can beobtained using these neighbors.B. Controller design based on MB modeling methodIn this paper, the following control law with a PID structure is considered: )()()()(2t y T T k t e T T k t u SD c I s c ∆+∆-=∆ （3）)()()(2t y K t y K t e K D P I ∆-∆-= （4）where e(t) denotes the control error signal defined bye(t) := r(t) − y(t). （5） r(t) denotes the reference signal. Also, kc, TI and TD respectively denote the proportional gain, the reset time and the derivative time, and Ts denotes the sampling interval. Here, KP , KI and KD included in Eq.(4) are derived by therelations P K =c k ，I K =c k s T /I T 和D K =c k D T /s T 。

PID控制器毕业设计外文翻译

附录一、英文原文PID controllerA proportional–integral–derivative controller (PID controller) is a genericcontrol loopfeedback mechanism(controller) widely used in industrial control systems –a PID is the most commonly used feedback controller. A PID controller calculates an "error" value as the difference between a measuredprocess variable and a desired setp oint. The controller attempts to minimize the error by adjusting the process control inputs. In the absence of knowledge of the underlying process, PID controllers are the best controllers.[1] However, for best performance, the PID parameters used in the calculation must be tuned according to the nature of the system – while the design is generic, the parameters depend on the specific system.The PID controller calculation (algorithm) involves three separate parameters, and is accordingly sometimes calledthree-term control: the proportional, the integral and derivative values, denoted P, I, and D. The proportionalvalue determines the reaction to the current error, the integral value determines the reaction based on the sum of recent errors, and the derivative value determines the reaction based on the rate at which the error has been changing. The weighted sum of these three actions is used to adjust the process via a control element such as the position of a control valve or the power supplyof a heating element. Heuristically, these values can be interpreted in terms of time: P depends on the present error, I on the accumulation of past errors, and D is a prediction offuture errors, based on current rate of change.By tuning the three constants in the PID controller algorithm, the controller can provide control action designed for specific process requirements. The response of the controller can be described in terms of the responsiveness of the controller to an error, the degree to which the controller overshoots the setpoint and the degree of system oscillation. Note that the use of the PID algorithm for control does not guarantee optimal control of the system or system stability. Some applications may require using only one or two modes to provide the appropriate system control. This is achieved by setting the gain of undesired control outputs to zero. A PID controller will be called a PI, PD, P or I controller in the absence of the respective control actions. PI controllers are fairly common, since derivative action is sensitive to measurement noise, whereas the absence of an integral value may prevent the system from reaching its target value due to the control action.Note: Due to the diversity of the field of control theory and application, many naming conventions for the relevant variables are in common use.Control loop basicsA familiar example of a control loop is the action taken when adjusting hot and cold faucet valves to maintain the faucet water at the desired temperature. This typically involves the mixing of two process streams, the hot and cold water. The person touches the water to sense or measure its temperature. Based on this feedback they perform a control action to adjust the hot and cold water valves until the process temperature stabilizes at the desired value.Sensing water temperature is analogous to taking a measurement of the process value or process variable (PV). The desired temperature is called the setpoint (SP). The input to the process (the water valve position) is called the manipulated variable (MV). The difference between the temperature measurement and the setpoint is the error (e), that quantifies whether the water is too hot or too cold and by how much.After measuring the temperature (PV), and then calculating the error, the controller decides when to change the tap position (MV) and by how much. When the controller first turns the valve on, they may turn the hot valve onlyslightly if warm water is desired, or they may open the valve all the way if very hot water is desired. This is an example of a simple proportional control. In the event that hot water does not arrive quickly, the controller may try to speed-up the process by opening up the hot water valve more-and-more as time goes by. This is an example of an integral control. By using only the proportional and integral control methods, it is possible that in some systems the water temperature may oscillate between hot and cold, because the controller is adjusting the valves too quickly and over-compensating or overshooting the set point.In the interest of achieving a gradual convergence at the desired temperature (SP), the controller may wish to dampthe anticipated future oscillations. So in order to compensate for this effect, the controller may elect to temper their adjustments. This can be thought of as a derivative control method.Making a change that is too large when the error is small is equivalent to a high gain controller and will lead to overshoot. If the controller were to repeatedly make changes that were too large and repeatedly overshoot the target, the output would oscillate around the setpoint in either a constant, growing, or decaying sinusoid. If the oscillations increase with time then the system is unstable, whereas if they decrease the system is stable. If the oscillations remain at a constant magnitude the system is marginally stable. A human would not do this because we are adaptive controllers, learning from the process history; however, simple PID controllers do not have the ability to learn and must be set up correctly. Selecting the correct gains for effective control is known as tuning the controller.If a controller starts from a stable state at zero error (PV = SP), then further changes by the controller will be in response to changes in other measured or unmeasured inputs to the process that impact on the process, and hence on the PV. Variables that impact on the process other than the MV are known as disturbances. Generally controllers are used to reject disturbances and/or implement setpoint changes. Changes in feed water temperature constitute a disturbance to the faucet temperature control process.In theory, a controller can be used to control any process which has a measurable output (PV), a known ideal value for that output (SP) and an input to the process (MV) that will affect the relevant PV. Controllers are used in industry to regulate temperature, pressure, flow rate, chemical composition, speed andpractically every other variable for which a measurement exists. Automobile cruise control is an example of a process which utilizes automated control.PID controllers are the controllers of choice for many of these applications, due to their well-grounded theory, established history, simplicity, and simple setup and maintenance requirements.HistoryPID controllers date to 1890s governor design.[1][5] PID controllers were subsequently developed in automatic ship steering. One of the earliest examples of a PID-type controller was developed by Elmer Sperry in 1911,[6] while the first published theoretical analysis of a PID controller was by Russian Americanengineer Nicolas Minorsky, in (Minorsky 1922). Minorsky was designing automatic steering systems for the US Navy, and based his analysis on observations of ahelmsman, observing that the helmsman controlled the ship not only based on the current error, but also on past error and current rate of change;[7] this was then made mathematical by Minorsky. The Navy ultimately did not adopt the system, due to resistance by personnel. Similar work was carried out and published by several others in the 1930s.Initially controllers were pneumatic, hydraulic, or mechanical, with electrical systems later developing, with wholly electrical systems developed following World War II.Minorsky's workIn detail, Minorsky's work proceeded as follows.[8] His goal was stability, not general control, which significantly simplified the problem. While proportional control provides stability against small disturbances, it was insufficient for dealing with a steady disturbance, notably a stiff gale (due to droop), which required adding the integral term. Finally, the derivative term was added to improve control. Trials were carried out on the USS New Mexico, with the controller controlling the angular velocity (not angle) of the rudder. PI control yielded sustained yaw (angular error) of ±2°, while adding D yielded yaw of ±1/6°, better than most helmsmen could achieve.Limitations of PID controlWhile PID controllers are applicable to many control problems, and often perform satisfactorily without any improvements or even tuning, they canperform poorly in some applications, and do not in general provide optimalcontrol. The fundamental difficulty with PID control is that it is a feedback system, with constant parameters, and no direct knowledge of the process, and thus overall performance is reactive and a compromise – while PID control is the best controller with no model of the process,[1] better performance can be obtained by incorporating a model of the process.The most significant improvement is to incorporate feed-forward control with knowledge about the system, and using the PID only to control error. Alternatively, PIDs can be modified in more minor ways, such as by changing the parameters (either gain scheduling in different use cases or adaptively modifying them based on performance), improving measurement (higher sampling rate, precision, and accuracy, and low-pass filtering if necessary), or cascading multiple PID controllers.PID controllers, when used alone, can give poor performance when the PID loop gains must be reduced so that the control system does not overshoot, oscillate or hunt about the control setpoint value. They also have difficulties in the presence of non-linearities, may trade off regulation versus response time, do not react to changing process behavior (say, the process changes after it has warmed up), and have lag in responding to large disturbances.LinearityAnother problem faced with PID controllers is that they are linear, and in particular symmetric. Thus, performance of PID controllers in non-linear systems (such as HV AC systems) is variable. For example, in temperature control, a common use case is active heating (via a heating element) but passive cooling (heating off, but no cooling), so overshoot can only be corrected slowly –it cannot be forced downward. In this case the PID should be tuned to be overdamped, to prevent or reduce overshoot, though this reduces performance (it increases settling time).Noise in derivativeA problem with the Derivative term is that small amounts of measurement or process noise can cause large amounts of change in the output. It is often helpful to filter the measurements with a low-pass filter in order to remove higher-frequency noise components. However, low-pass filtering and derivative control can cancel each other out, so reducing noise by instrumentation means isa much better choice. Alternatively, a nonlinear median filter may be used, which improves the filtering efficiency and practical performance [9]. In some case, the differential band can be turned off in many systems with little loss of control. This is equivalent to using the PID controller as a PIcontroller.Feed-forwardThe control system performance can be improved by combining the feedback (or closed-loop) control of a PID controller with feed-forward (or open-loop) control. Knowledge about the system (such as the desired acceleration and inertia) can be fed forward and combined with the PID output to improve the overall system performance. The feed-forward value alone can often provide the major portion of the controller output. The PID controller can be used primarily to respond to whatever difference or error remains between the setpoint (SP) and the actual value of the process variable (PV). Since the feed-forward output is not affected by the process feedback, it can never cause the control system to oscillate, thus improving the system response and stability.For example, in most motion control systems, in order to accelerate a mechanical load under control, more force or torque is required from the prime mover, motor, or actuator. If a velocity loop PID controller is being used to control the speed of the load and command the force or torque being applied by the prime mover, then it is beneficial to take the instantaneous acceleration desired for the load, scale that value appropriately and add it to the output of the PID velocity loop controller.This means that whenever the load is being accelerated or decelerated, a proportional amount of force is commanded from the prime mover regardless of the feedback value. The PID loop in this situation uses the feedback information to effect any increase or decrease of the combined output in order to reduce the remaining difference between the process setpoint and the feedback value. Working together, the combined open-loop feed-forward controller and closed-loop PID controller can provide a more responsive, stable and reliable control system.Other improvementsIn addition to feed-forward, PID controllers are often enhanced through methods such as PID gain scheduling(changing parameters in different operating conditions), fuzzy logic or computational verb logic[10][11] . Further practicalapplication issues can arise from instrumentation connected to the controller. A high enough sampling rate, measurement precision, and measurement accuracy are required to achieve adequate control performance.Cascade controlOne distinctive advantage of PID controllers is that two PID controllers can be used together to yield better dynamic performance. This is called cascaded PID control. In cascade control there are two PIDs arranged with one PID controlling the set point of another. A PID controller acts as outer loop controller, which controls the primary physical parameter, such as fluid level or velocity. The other controller acts as inner loop controller, which reads the output of outer loop controller as set point, usually controlling a more rapid changing parameter, flowrate or acceleration. It can be mathematically proven[citation needed] that the working frequency of the controller is increased and the time constant of the object is reduced by using cascaded PID controller.Physical implementation of PID controlIn the early history of automatic process control the PID controller was implemented as a mechanical device. These mechanical controllers used a lever, spring and a mass and were often energized by compressed air. These pneumaticcontrollers were once the industry standard.Electronic analog controllers can be made from a solid-state or tubeamplifier, a capacitor and a resistance. Electronic analog PID control loops were often found within more complex electronic systems, for example, the head positioning of a disk drive, the power conditioning of a power supply, or even the movement-detection circuit of a modern seismometer. Nowadays, electronic controllers have largely been replaced by digital controllers implemented with micro controllers or FPGAs.Most modern PID controllers in industry are implemented in programmable logic controllers (PLCs) or as a panel-mounted digital controller. Software implementations have the advantages that they are relatively cheap and are flexible with respect to the implementation of the PID algorithm.Variable voltages may be applied by the time proportioning form of Pulse-width modulation (PWM) – a cycle time is fixed, and variation is achieved by varying the proportion of the time during this cycle that the controller outputs +1 (or −1) instead of 0. On a digital system the possible proportions are discrete –e.g.,incrementsof.1second within a 2 second cycle time yields 20 possible steps: percentage increments of 5% –so there is a discretization error, but for high enough time resolution this yields satisfactory performance. Alternative nomenclature and PID formsIdeal versus standard PID formThe form of the PID controller most often encountered in industry, and the one most relevant to tuning algorithms is the standard form. In this form the Kp gain is applied to the Iout, and Dout terms, yielding:WhereTi is the integral timeTd is the derivative timeIn the ideal parallel form, shown in the controller theory sectionthe gain parameters are related to the parameters of the standard form through and . This parallel form, where the parameters are treated as simple gains, is the most general and flexible form. However, it is also the form where the parameters have the least physical interpretation and is generally reserved for theoretical treatment of the PID controller. The standard form, despite being slightly more complex mathematically, is more common in industry.Laplace form of the PID controllerSometimes it is useful to write the PID regulator in Laplace transform form:Having the PID controller written in Laplace form and having the transfer function of the controlled system makes it easy to determine the closed-loop transfer function of the system.Series/interacting formAnother representation of the PID controller is the series, or interacting formwhere the parameters are related to the parameters of the standard form through , , andWith.This form essentially consists of a PD and PI controller in series, and it made early (analog) controllers easier to build. When the controllers later became digital, many kept using the interacting form.Discrete implementationThe analysis for designing a digital implementation of a PID controller in a Microcontroller (MCU) or FPGA device requires the standard form of the PID controller to be discretised [12]. Approximations for first-order derivatives are made by backward finite differences. The integral term is discretised, with a sampling time Δt,as follows,The derivative term is approximated as,Thus, a velocity algorithm for implementation of the discretised PID controller in a MCU is obtained by differentiating u(t), using the numerical definitions of the first and second derivative and solving for u(tk) and finally obtaining:二、英文翻译PID控制器一个比例，积分，微分控制（PID控制器）是一个通用的控制回路，反馈控制器被广泛应用于工业控制系统，一个PID是最常用的反馈控制器。

pid简介及英语翻译

PID ControlIntroductionThe PID controller is the most common form of feedback. It was an essential element of early governors and it became the standard tool when process control emerged in the 1940s. In process control today, more than 95% of the control loops are of PID type, most loops are actually PI control. PID controllers are today found in all areas where control is used. The controllers come in many different forms. There are standalone systems in boxes for one or a few loops, which are manufactured by the hundred thousands yearly. PID control is an important ingredient of a distributed control system. The controllers are also embedded in many special purpose control systems. PID control is often combined with logic, sequential functions, selectors, and simple function blocks to build the complicated automation systems used for energy production, transportation, and manufacturing. Many sophisticated control strategies, such as model predictive control, are also organized hierarchically. PID control is used at the lowest level; the multivariable controller gives the set points to the controllers at the lower level. The PID controller can thus be said to be the “bread and butter of control engineering. It is an important component in every control engineer ’s tool box.PID controllers have survived many changes in technology, from mechanics and pneumatics to microprocessors via electronic tubes, transistors, integrated circuits. The microprocessor has had a dramatic influence the PID controller. Practically all PID controllers made today are based on microprocessors. This has given opportunities to provide additional features like automatic tuning, gain scheduling, and continuous adaptation.The AlgorithmWe will start by summarizing the key features of the PID controller. The “textbook ” version of the PID algorithm is described by:()()()()⎪⎪⎭⎫ ⎝⎛++=⎰dt t de d e t e K t u T T dti 01ττ 6.1 where y is the measured process variable, r the reference variable,u is the control signal and e is the control error（e =y−y）. Thespreference variable is often called the set point. The control signal is thus a sum of three terms: the P-term （which is proportional to the error）, the I-term （which is proportional to the integral of the error）, and the D-term （which is proportional to the derivative of the error）. The controller parameters are proportional gain K, integral time T i, and derivative time T d. The integral, proportional and derivative part can be interpreted as control actions based on the past, the present and the future as is illustrated in Figure 2.2. The derivative part can also be interpreted as prediction by linear extrapolation as is illustrated in Figure 2.2. The action of the different terms can be illustrated by the following figures which show the response to step changes in the reference value in a typical case.Effects of Proportional, Integral and Derivative ActionProportional control is illustrated in Figure 6.1. The controller is given by D6.1E with T i= and T d=0. The figure shows that there is always a steady state error in proportional control. The error will decrease with increasing gain, but the tendency towards oscillation will also increase.Figure 6.2 illustrates the effects of adding integral. It follows from D6.1E that the strength of integral action increases with decreasing . The figure shows that the steady state error disappearsintegral time Tiwhen integral action is used. Compare with the discussion of the “magic of integral action” in Section 2.2. The tendency for oscillation also. The properties of derivative action are increases with decreasing Tiillustrated in Figure 6.3.Figure 6.3 illustrates the effects of adding derivative action. Theare chosen so that the closed loop system is oscillatory. parameters K and TiDamping increases with increasing derivative time, but decreases again when derivative time becomes too large. Recall that derivative action can be interpreted as providing prediction by linear extrapolation over the . Using this interpretation it is easy to understand that derivativetime Tdaction does not help if the prediction time Tis too large. In Figured6.3 the period of oscillation is about 6 s for the system without derivative Chapter 6. PID ControlFigure 6.1Figure 6.2Derivative actions cease to be effective when T d is larger than a 1 s (one sixth of the period). Also notice that the period of oscillation increases when derivative time is increased.There is much more to PID than is revealed by （6.1）. A faithful implementation of the equation will actually not result in a good controller. To obtain a good PID controller it is also necessary to consider 。

自动化英语论文——温度控制简介和PID控制器

自动化英语论文——温度控制简介和PID控制器Introductions to temperature control and PID controllers Process control system.Automatic process control is concerned with maintaining process variables temperatures pressures flows compositions, and the like at some desired operation value. Processes are dynamic in nature. Changes are always occurring, and if actions are notthose related to safety, product taken, the important process variables-quality, and production rates-will not achieve design conditions.In order to fix ideas, let us consider a heat exchanger in which a process stream is heated by condensing steam. The process is sketched in Fig.1Fig. 1 Heat exchangerThe purpose of this unit is to heat the process fluid from someinlet temperature, Ti(t), up to a certain desired outlet temperature,T(t). As mentioned, the heating medium is condensing steam.The energy gained by the process fluid is equal to the heat released by the steam, provided there are no heat losses to surroundings, iiithat is, the heat exchanger and piping are well insulated.In this process there are many variables that can change, causingthe outlet temperature to deviate from its desired value. [21 If this happens, some action must be taken to correct for this deviation. Thatis, the objective is to control the outlet process temperature tomaintain its desired value.One way to accomplish this objective is by first measuring the temperature T(t) , then comparing it to its desired value, and, based on this comparison, deciding what to do to correct for any deviation. The flow of steam can be used to correct for the deviation. This is, if the temperature is above its desired value, then the steam valve can be throttled back to cut the stearr flow (energy) to the heat exchanger. If the temperature is below its desired value, then the steam valve couldbe opened some more to increase the steam flow (energy) to the exchanger. All of these can be done manually by the operator, and since the procedure is fairly straightforward, it should present no problem. However, since in most process plants there are hundreds of variablesthat must bemaintained at some desired value, this correction procedure would required a tremendous number of operators. Consequently, we would liketo accomplish this control automatically. That is, we want to have instnnnents that control the variables wJtbom requ)ring interventionfrom the operator. (si This is what we mean by automatic process control.To accomplish ~his objective a control system must be designed and implemented. A possible control system and its basic components are shown in Fig.2.Fig. 2 Heat exchanger control loopThe first thing to do is to measure the outlet temperaVare of the process stream. A sensor (thermocouple, thermistors, etc) does this. This sensor is connected physically to a transmitter, which takes the output from the sensor and converts it to a signal strong enough to be transmitter to a controller. The controller then receives the signal, which is related to the temperature, and compares it with desired value. Depending on this comparison, the controller decides what to do to maintain the temperature at its desired value. Base on this decision, the controller then sends another signal to final control element, which in turn manipulates the steam flow.The preceding paragraph presents the four basic components of all control systems. They are(1) sensor, also often called the primary element.(2) transmitter, also called the secondary element.(3) controller, the "brain" of the control system.(4) final control system, often a control valve but not always. Other common final control elements are variable speed pumps, conveyors, and electric motors.The importance of these components is that they perform the three basic operations that must be present in every control system. These operations are(1) Measurement (M) : Measuring the variable to be controlled is usually done by the combination of sensor and transmitter.(2) Decision (D): Based on the measurement, the controller must then decide what to do to maintain the variable at its desired value.(3) Action (A): As a result of the controller's decision, the system must then take an action. This is usually accomplished by the final controlelement.As mentioned, these three operations, M, D, and A, must be presentin every control system.PID controllers can be stand-alone controllers (also called single loop controllers), controllers in PLCs, embedded controllers, or software in Visual Basic or C# computer programs.PID controllers are process controllers with the followingAnalog input (also known as characteristics:Continuous processcontrol"measuremem" or "Process Variable" or "PV")Analog output (referredto simply as "output") Setpoint (SP)Proportional (P), Integral (I),and/or Derivative (D) constantsExamples of "continuous process control" are temperature, pressure, flow, and level control. For example, controlling the heating of a tank.For simple control, you have two temperature limit sensors (one low and one high) and then switch the heater on when the low temperature limit sensor tums on and then mm the heater off when the temperature rises to the high temperature limit sensor. This is similar to most home air conditioning & heating thermostats.In contrast, the PID controller would receive input as the actual temperature and control a valve that regulates the flow of gas to the heater. The PID controller automatically finds the correct (constant) flow of gas to the heater that keeps the temperature steady at the setpoint. Instead of the temperature bouncing back and forth between two points, the temperature is held steady. If the setpoint is lowered, then the PID controller automatically reduces the amount of gas flowing to the heater. If the setpoint is raised, then the PID controller automatically increases the amount of gas flowing to the heater. Likewise the PID controller would automatically for hot, sunny days (when it is hotter outside the heater) and for cold, cloudy days.The analog input (measurement) is called the "process variable" or "PV". You want the PV to be a highly accurate indication of the process parameter you are trying to control. For example, if you want to maintain a temperature of + or -- one degree then we typically strivefor at least ten times that or one-tenth of a degree. If the analog input is a 12 bit analog input and the temperature range for the sensor is 0 to 400 degrees then our "theoretical" accuracy is calculated to be 400 degrees divided by 4,096 (12 bits) =0.09765625 degrees. [~] We say"theoretical" because it would assume there was no noise and error in our temperature sensor, wiring, and analog converter. There are other assumptions such as linearity, etc.. The point being--with 1/10 of a degree "theoretical" accuracy--even with the usual amount of noise and other problems-- one degree of accuracy should easily be attainable.The analog output is often simply referred to as "output". Oftenthis is given as 0~100 percent. In this heating example, it would mean the valveis totally closed (0%) or totally open (100%).The setpoint (SP) is simply--what process value do you want. In this example--what temperature do you want the process at?The PID controller's job is to maintain the output at a level sothat there is no difference (error) between the process variable (PV) and the setpoint (SP).In Fig. 3, the valve could be controlling the gas going to a heater, the chilling of a cooler, the pressure in a pipe, the flow through a pipe, the level in a tank, or any other process control system. What the PID controller is looking at is the difference (or "error") between the PV and the SP.SETPOINT P，I，&DCONSTANTSDifference error PID controlalgorithmprocess outputvariableFig .3 PIDcontrolIt looks at the absolute error and the rate of change of error. Absolute error means--is there a big difference in the PV and SP or a little difference? Rate of change of error means--is the difference between the PV or SP getting smaller or larger as time goes on.When there is a "process upset", meaning, when the process variableor the setpoint quickly changes--the PID controller has to quickly change the output to get the process variable back equal to the setpoint. If you have a walk-in cooler with a PID controller and someone opens the door and walks in, the temperature (process variable) could rise very quickly. Therefore the PID controller has to increase the cooling (output) to compensate for this rise in temperature.Once the PID controller has the process variable equal to the setpoint, a good PID controller will not vary the output. You want the output to be very steady (not changing) . If the valve (motor, or other control element) is constantly changing, instead of maintaining a constant value, this could cause more wear on the control element.So there are these two contradictory goals. Fast response (fast change in output) when there is a "process upset", but slow response (steady output) when the PV is close to the setpoint.Note that the output often goes past (over shoots) the steady-state output to get the process back to the setpoint. For example, a coolermay normally have its cooling valve open 34% to maintain zero degrees (afterthe cooler has been closed up and the temperature settled down). If someone opens the cooler, walks in, walks around to find something, then walks back out, and then closes the cooler door--the PID controller is freaking out because the temperature may have raised 20 degrees! So it may crank the cooling valve open to 50, 75, or even 100 percent--to hurry up and cool the cooler back down--before slowly closing the cooling valve backdown to 34 percent.。

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