外文翻译---自动控制基础
自动化专业毕业论文外文文献翻译
目录Part 1 PID type fuzzy controller and parameters adaptive method (1)Part 2 Application of self adaptation fuzzy-PID control for main steam temperature control system in power station (7)Part 3 Neuro-fuzzy generalized predictive control of boiler steam temperature ..................................................................... (13)Part 4 为Part3译文：锅炉蒸汽温度模糊神经网络的广义预测控制21Part 1 PID type fuzzy controller and Parametersadaptive methodWu zhi QIAO, Masaharu MizumotoAbstract: The authors of this paper try to analyze the dynamic behavior of the product-sum crisp type fuzzy controller, revealing that this type of fuzzy controller behaves approximately like a PD controller that may yield steady-state error for the control system. By relating to the conventional PID control theory, we propose a new fuzzy controller structure, namely PID type fuzzy controller which retains the characteristics similar to the conventional PID controller. In order to improve further the performance of the fuzzy controller, we work out a method to tune the parameters of the PID type fuzzy controller on line, producing a parameter adaptive fuzzy controller. Simulation experiments are made to demonstrate the fine performance of these novel fuzzy controller structures.Keywords: Fuzzy controller; PID control; Adaptive control1. IntroductionAmong various inference methods used in the fuzzy controller found in literatures , the most widely used ones in practice are the Mamdani method proposed by Mamdani and his associates who adopted the Min-max compositional rule of inference based on an interpretation of a control rule as a conjunction of the antecedent and consequent, and the product-sum method proposed by Mizumoto who suggested to introduce the product and arithmetic mean aggregation operators to replace the logical AND (minimum) and OR (maximum) calculations in the Min-max compositional rule of inference.In the algorithm of a fuzzy controller, the fuzzy function calculation is also a complicated and time consuming task. Tagagi and Sugeno proposed a crisp type model in which the consequent parts of the fuzzy control rules are crisp functional representation or crisp real numbers in the simplified case instead of fuzzy sets . With this model of crisp real number output, the fuzzy set of the inference consequence willbe a discrete fuzzy set with a finite number of points, this can greatly simplify the fuzzy function algorithm.Both the Min-max method and the product-sum method are often applied with the crisp output model in a mixed manner. Especially the mixed product-sum crisp model has a fine performance and the simplest algorithm that is very easy to be implemented in hardware system and converted into a fuzzy neural network model. In this paper, we will take account of the product-sum crisp type fuzzy controller.2. PID type fuzzy controller structureAs illustrated in previous sections, the PD function approximately behaves like a parameter time-varying PD controller. Since the mathematical models of most industrial process systems are of type, obviously there would exist an steady-state error if they are controlled by this kind of fuzzy controller. This characteristic has been stated in the brief review of the PID controller in the previous section.If we want to eliminate the steady-state error of the control system, we can imagine to substitute the input (the change rate of error or the derivative of error) of the fuzzy controller with the integration of error. This will result the fuzzy controller behaving like a parameter time-varying PI controller, thus the steady-state error is expelled by the integration action. However, a PI type fuzzy controller will have a slow rise time if the P parameters are chosen small, and have a large overshoot if the P or I parameters are chosen large. So there may be the time when one wants to introduce not only the integration control but the derivative control to the fuzzy control system, because the derivative control can reduce the overshoot of the system's response so as to improve the control performance. Of course this can be realized by designing a fuzzy controller with three inputs, error, the change rate of error and the integration of error. However, these methods will be hard to implement in practice because of the difficulty in constructing fuzzy control rules. Usually fuzzy control rules are constructed by summarizing the manual control experience of an operator who has been controlling the industrial process skillfully and successfully. The operator intuitively regulates the executor to control the process by watching theerror and the change rate of the error between the system's output and the set-point value. It is not the practice for the operator to observe the integration of error. Moreover, adding one input variable will greatly increase the number of control rules, the constructing of fuzzy control rules are even more difficult task and it needs more computation efforts. Hence we may want to design a fuzzy controller that possesses the fine characteristics of the PID controller by using only the error and the change rate of error as its inputs.One way is to have an integrator serially connected to the output of the fuzzy controller as shown in Fig. 1. In Fig. 1,1K and 2K are scaling factors for e and ~ respectively, and fl is the integral constant. In the proceeding text, for convenience, we did not consider the scaling factors. Here in Fig. 2, when we look at the neighborhood of NODE point in the e - ~ plane, it follows from (1) that the control input to the plant can be approximated by(1)Hence the fuzzy controller becomes a parameter time-varying PI controller, itsequivalent proportional control and integral control components are BK2D and ilK1 P respectively. We call this fuzzy controller as the PI type fuzzy controller (PI fc). We can hope that in a PI type fuzzy control system, the steady-state error becomes zero.To verify the property of the PI type fuzzy controller, we carry out some simulation experiments. Before presenting the simulation, we give a description of the simulation model. In the fuzzy control system shown in Fig. 3, the plant model is a second-order and type system with the following transfer function:)1)(1()(21++=s T s T K s G (2) Where K = 16, 1T = 1, and 2T = 0.5. In our simulation experiments, we use thediscrete simulation method, the results would be slightly different from that of a continuous system, the sampling time of the system is set to be 0.1 s. For the fuzzy controller, the fuzzy subsets of e and d are defined as shown in Fig. 4. Their coresThe fuzzy control rules are represented as Table 1. Fig. 5 demonstrates the simulation result of step response of the fuzzy control system with a Pl fc. We can see that the steady-state error of the control system becomes zero, but when the integration factor fl is small, the system's response is slow, and when it is too large, there is a high overshoot and serious oscillation. Therefore, we may want to introduce the derivative control law into the fuzzy controller to overcome the overshoot and instability. We propose a controller structure that simply connects the PD type and the PI type fuzzy controller together in parallel. We have the equivalent structure of that by connecting a PI device with the basic fuzzy controller serially as shown in Fig.6. Where ~ is the weight on PD type fuzzy controller and fi is that on PI type fuzzy controller, the larger a/fi means more emphasis on the derivative control and less emphasis on the integration control, and vice versa. It follows from (7) that the output of the fuzzy controller is(3)3. The parameter adaptive methodThus the fuzzy controller behaves like a time-varying PID controller, its equivalent proportional control, integral control and derivative control components are respectively. We call this new controller structure a PID type fuzzy controller (PID fc). Figs. 7 and 8 are the simulation results of the system's step response of such control system. The influence of ~ and fl to the system performance is illustrated. When ~ > 0 and/3 = 0, meaning that the fuzzy controller behaves like PD fc, there exist a steady-state error. When ~ = 0 and fl > 0, meaning that the fuzzy controller behaves like a PI fc, the steady-state error of the system is eliminated but there is a large overshoot and serious oscillation.When ~ > 0 and 13 > 0 the fuzzy controller becomes a PID fc, the overshoot is substantially reduced. It is possible to get a comparatively good performance by carefully choosing the value of αandβ.4. ConclusionsWe have studied the input-output behavior of the product-sum crisp type fuzzy controller, revealing that this type of fuzzy controller behaves approximately like a parameter time-varying PD controller. Therefore, the analysis and designing of a fuzzy control system can take advantage of the conventional PID control theory. According to the coventional PID control theory, we have been able to propose some improvement methods for the crisp type fuzzy controller.It has been illustrated that the PD type fuzzy controller yields a steady-state error for the type system, the PI type fuzzy controller can eliminate the steady-state error. We proposed a controller structure, that combines the features of both PD type and PI type fuzzy controller, obtaining a PID type fuzzy controller which allows the control system to have a fast rise and a small overshoot as well as a short settling time.To improve further the performance of the proposed PID type fuzzy controller, the authors designed a parameter adaptive fuzzy controller. The PID type fuzzy controller can be decomposed into the equivalent proportional control, integral control and the derivative control components. The proposed parameter adaptive fuzzy controller decreases the equivalent integral control component of the fuzzy controller gradually with the system response process time, so as to increase the damping of the system when the system is about to settle down, meanwhile keeps the proportional control component unchanged so as to guarantee quick reaction against the system's error. With the parameter adaptive fuzzy controller, the oscillation of the system is strongly restrained and the settling time is shortened considerably.We have presented the simulation results to demonstrate the fine performance of the proposed PID type fuzzy controller and the parameter adaptive fuzzy controller structure.Part 2 Application of self adaptation fuzzy-PID control for main steam temperature control system inpower stationZHI-BIN LIAbstract: In light of the large delay, strong inertia, and uncertainty characteristics of main steam temperature process, a self adaptation fuzzy-PID serial control system is presented, which not only contains the anti-disturbance performance of serial control, but also combines the good dynamic performance of fuzzy control. The simulation results show that this control system has more quickly response, better precision and stronger anti-disturbance ability．Keywords：Main steam temperature；Self adaptation；Fuzzy control；Serial control1. IntroductionThe boiler superheaters of modem thermal power station run under the condition of high temperature and high pressure, and the superheater’s temperature is highest in the steam channels．so it has important effect to the running of the whole thermal power station．If the temperature is too high, it will be probably burnt out. If the temperature is too low ,the efficiency will be reduced So the main steam temperature mast be strictly controlled near the given value．Fig l shows the boiler main steam temperature system structure.Fig.1 boiler main steam temperature systemIt can be concluded from Fig l that a good main steam temperature controlsystem not only has adequately quickly response to flue disturbance and load fluctuation, but also has strong control ability to desuperheating water disturbance. The general control scheme is serial PID control or double loop control system with derivative. But when the work condition and external disturbance change large, the performance will become instable. This paper presents a self adaptation fuzzy-PID serial control system. which not only contains the anti-disturbance performance of serial control, but also combines the good dynamic character and quickly response of fuzzy control ．1. Design of Control SystemThe general regulation adopts serial PID control system with load feed forward ．which assures that the main steam temperature is near the given value 540℃in most condition ．If parameter of PID control changeless and the work condition and external disturbance change large, the performance will become in stable ．The fuzzy control is fit for controlling non-linear and uncertain process. The general fuzzy controller takes error E and error change ratio EC as input variables ．actually it is a non-linear PD controller, so it has the good dynamic performance ．But the steady error is still in existence. In linear system theory, integral can eliminate the steady error. So if fuzzy control is combined with PI control, not only contains the anti-disturbance performance of serial control, but also has the good dynamic performance and quickly response.In order to improve fuzzy control self adaptation ability, Prof ．Long Sheng-Zhao and Wang Pei-zhuang take the located in bringing forward a new idea which can modify the control regulation online ．This regulation is:]1,0[,)1(∈-+=αααEC E UThis control regulation depends on only one parameter α.Once αis fixed ．the weight of E and EC will be fixed and the self adaptation ability will be very small ．It was improved by Prof. Li Dong-hui and the new regulation is as follow;]1,0[,,,3,)1(2,)1(1,)1(0,)1({321033221100∈±=-+±=-+±=-+=-+=ααααααααααααE EC E E EC E E EC E E EC E UBecause it is very difficult to find a self of optimum parameter, a new method is presented by Prof ．Zhou Xian-Lan, the regulation is as follow:)0(),ex p(12>--=k ke αBut this algorithm still can not eliminate the steady error ．This paper combines this algorithm with PI control ，the performance is improved ．2. Simulation of Control System3.1 Dynamic character of controlled objectPapers should be limited to 6 pages Papers longer than 6 pages will be subject to extra fees based on their length ．Fig .2 main steam temperature control system structureFig 2 shows the main steam temperature control system structure ，)(),(21s W s W δδare main controller and auxiliary controller,)(),(21s W s W o o are characters of the leading and inertia sections,)(),(21s W s W H H are measure unit.3.2 Simulation of the general serial PID control systemThe simulation of the general serial PID control system is operated by MATLAB, the simulation modal is as Fig.3.Setp1 and Setp2 are the given value disturbance and superheating water disturb & rice .PID Controller1 and PID Controller2 are main controller and auxiliary controller ．The parameter value which comes from references is as follow ：667.37,074.0,33.31)(25)(111111122===++===D I p D I p p k k k s k sk k s W k s W δδFig.3. the general PID control system simulation modal3.3 Simulation of self adaptation fuzzy-PID control system SpacingThe simulation modal is as Fig 4.Auxiliary controller is:25)(22==p k s W δ.Main controller is Fuzzy-PI structure, and the PI controller is:074.0,33.31)(11111==+=I p I p k k s k k s W δFuzzy controller is realized by S-function, and the code is as fig.5.Fig.4. the fuzzy PID control system simulation modalFig 5 the S-function code of fuzzy control3.4 Comparison of the simulationGiven the same given value disturbance and the superheating water disturbance，we compare the response of fuzzy-PID control system with PID serial control system. The simulation results are as fig.6-7.From Fig6-7,we can conclude that the self adaptation fuzzy-PID control system has the more quickly response, smaller excess and stronger anti-disturbance．4. Conclusion(1)Because it combines the advantage of PID controller and fuzzy controller, theself adaptation fuzzy-PID control system has better performance than the general PID serial control system.(2)The parameter can self adjust according to the error E value. so this kind of controller can harmonize quickly response with system stability．Part 3 Neuro-fuzzy generalized predictive controlof boiler steam temperatureXiangjie LIU, Jizhen LIU, Ping GUANAbstract: Power plants are nonlinear and uncertain complex systems. Reliable control of superheated steam temperature is necessary to ensure high efficiency and high load-following capability in the operation of modern power plant. A nonlinear generalized predictive controller based on neuro-fuzzy network (NFGPC) is proposed in this paper. The proposed nonlinear controller is applied to control the superheated steam temperature of a 200MW power plant. From the experiments on the plant and the simulation of the plant, much better performance than the traditional controller is obtained.Keywords: Neuro-fuzzy networks; Generalized predictive control; Superheated steam temperature1. IntroductionContinuous process in power plant and power station are complex systems characterized by nonlinearity, uncertainty and load disturbance. The superheater is an important part of the steam generation process in the boiler-turbine system, where steam is superheated before entering the turbine that drives the generator. Controlling superheated steam temperature is not only technically challenging, but also economically important.From Fig.1,the steam generated from the boiler drum passes through the low-temperature superheater before it enters the radiant-type platen superheater. Water is sprayed onto the steam to control the superheated steam temperature in both the low and high temperature superheaters. Proper control of the superheated steam temperature is extremely important to ensure the overall efficiency and safety of the power plant. It is undesirable that the steam temperature is too high, as it can damage the superheater and the high pressure turbine, or too low, as it will lower the efficiency of the power plant. It is also important to reduce the temperaturefluctuations inside the superheater, as it helps to minimize mechanical stress that causes micro-cracks in the unit, in order to prolong the life of the unit and to reduce maintenance costs. As the GPC is derived by minimizing these fluctuations, it is amongst the controllers that are most suitable for achieving this goal.The multivariable multi-step adaptive regulator has been applied to control the superheated steam temperature in a 150 t/h boiler, and generalized predictive control was proposed to control the steam temperature. A nonlinear long-range predictive controller based on neural networks is developed into control the main steam temperature and pressure, and the reheated steam temperature at several operating levels. The control of the main steam pressure and temperature based on a nonlinear model that consists of nonlinear static constants and linear dynamics is presented in that.Fig.1 The boiler and superheater steam generation process Fuzzy logic is capable of incorporating human experiences via the fuzzy rules. Nevertheless, the design of fuzzy logic controllers is somehow time consuming, as the fuzzy rules are often obtained by trials and errors. In contrast, neural networks not only have the ability to approximate non-linear functions with arbitrary accuracy, they can also be trained from experimental data. The neuro-fuzzy networks developed recently have the advantages of model transparency of fuzzy logic and learning capability of neural networks. The NFN is have been used to develop self-tuning control, and is therefore a useful tool for developing nonlinear predictive control. Since NFN is can be considered as a network that consists of several local re-gions, each of which contains a local linear model, nonlinear predictive control based onNFN can be devised with the network incorporating all the local generalized predictive controllers (GPC) designed using the respective local linear models. Following this approach, the nonlinear generalized predictive controllers based on the NFN, or simply, the neuro-fuzzy generalized predictive controllers (NFG-PCs)are derived here. The proposed controller is then applied to control the superheated steam temperature of the 200MW power unit. Experimental data obtained from the plant are used to train the NFN model, and from which local GPC that form part of the NFGPC is then designed. The proposed controller is tested first on the simulation of the process, before applying it to control the power plant.2. Neuro-fuzzy network modellingConsider the following general single-input single-output nonlinear dynamic system:),1(),...,(),(),...,1([)(''+-----=uy n d t u d t u n t y t y f t y ∆+--/)()](),...,1('t e n t e t e e (1)where f[.]is a smooth nonlinear function such that a Taylor series expansion exists, e(t)is a zero mean white noise and Δis the differencing operator,''',,e u y n n n and d are respectively the known orders and time delay of the system. Let the local linear model of the nonlinear system (1) at the operating point )(t o be given by the following Controlled Auto-Regressive Integrated Moving Average (CARIMA) model:)()()()()()(111t e z C t u z B z t y z A d ----+∆= (2) Where )()(),()(1111----∆=z andC z B z A z A are polynomials in 1-z , the backward shift operator. Note that the coefficients of these polynomials are a function of the operating point )(t o .The nonlinear system (1) is partitioned into several operating regions, such that each region can be approximated by a local linear model. Since NFN is a class of associative memory networks with knowledge stored locally, they can be applied to model this class of nonlinear systems. A schematic diagram of the NFN is shown in Fig.2.B-spline functions are used as the membership functions in theNFN for the following reasons. First, B-spline functions can be readily specified by the order of the basis function and the number of inner knots. Second, they are defined on a bounded support, and the output of the basis function is always positive, i.e.,],[,0)(j k j j k x x λλμ-∉=and ],[,0)(j k j j k x x λλμ-∈>.Third, the basis functions form a partition of unity, i.e.,.][,1)(min,∑∈≡j mam j k x x x x μ(3)And fourth, the output of the basis functions can be obtained by a recurrence equation.Fig. 2 neuro-fuzzy network The membership functions of the fuzzy variables derived from the fuzzy rules can be obtained by the tensor product of the univariate basis functions. As an example, consider the NFN shown in Fig.2, which consists of the following fuzzy rules: IF operating condition i (1x is positive small, ... , and n x is negative large),THEN the output is given by the local CARIMA model i:...)()(ˆ...)1(ˆ)(ˆ01+-∆+-++-=d t u b n t y a t y a t yi i a i in i i i a )(...)()(c i in i b i in n t e c t e n d t u b c b -+++--∆+ (4)or )()()()()(ˆ)(111t e z C t u z B z t yz A i i i i d i i ----+∆= (5) Where )()(),(111---z andC z B z A i i i are polynomials in the backward shift operator 1-z , and d is the dead time of the plant,)(t u i is the control, and )(t e i is a zero mean independent random variable with a variance of 2δ. The multivariate basis function )(k i x a is obtained by the tensor products of the univariate basis functions,p i x A a nk k i k i ,...,2,1,)(1==∏=μ (6)where n is the dimension of the input vector x , and p , the total number of weights in the NFN, is given by,∏=+=nk i i k R p 1)( (7)Where i k and i R are the order of the basis function and the number of inner knots respectively. The properties of the univariate B-spline basis functions described previously also apply to the multivariate basis function, which is defined on the hyper-rectangles. The output of the NFN is,∑∑∑=====p i i i p i ip i i i a y aa yy 111ˆˆˆ (8) 3. Neuro-fuzzy modelling and predictive control of superheatedsteam temperatureLet θbe the superheated steam temperature, and θμ, the flow of spray water to the high temperature superheater. The response of θcan be approximated by a second order model:The linear models, however, only a local model for the selected operating point. Since load is the unique antecedent variable, it is used to select the division between the local regions in the NFN. Based on this approach, the load is divided into five regions as shown in Fig.3,using also the experience of the operators, who regard a load of 200MW as high,180MW as medium high,160MW as medium,140MW as medium low and 120MW as low. For a sampling interval of 30s , the estimated linear local models )(1-z A used in the NFN are shown in Table 1.Fig. 3 Membership function for local modelsTable 1 Local CARIMA models in neuro-fuzzy modelCascade control scheme is widely used to control the superheated steam temperature. Feed forward control, with the steam flow and the gas temperature as inputs, can be applied to provide a faster response to large variations in these two variables. In practice, the feed forward paths are activated only when there are significant changes in these variables. The control scheme also prevents the faster dynamics of the plant, i.e., the spray water valve and the water/steam mixing, from affecting the slower dynamics of the plant, i.e., the high temperature superheater. With the global nonlinear NFN model in Table 1, the proposed NFGPC scheme is shown in Fig.4.Fig. 4 NFGPC control of superheated steam temperature with feed-for-ward control.As a further illustration, the power plant is simulated using the NFN model given in Table 1,and is controlled respectively by the NFGPC, the conventional linear GPC controller, and the cascaded PI controller while the load changes from 160MW to 200MW.The conventional linear GPC controller is the local controller designed for the“medium”operating region. The results are shown in Fig.5,showing that, as expected, the best performance is obtained from the NFGPC as it is designed based on a more accurate process model. This is followed by the conventional linear GPC controller. The performance of the conventional cascade PI controller is the worst, indicating that it is unable to control satisfactory the superheated steam temperature under large load changes. This may be the reason for controlling the power plant manually when there are large load changes.Fig.5 comparison of the NFGPC, conventional linear GPC, and cascade PI controller.4. ConclusionsThe modeling and control of a 200 MW power plant using the neuro-fuzzy approach is presented in this paper. The NFN consists of five local CARIMA models.The out-put of the network is the interpolation of the local models using memberships given by the B-spline basis functions. The proposed NFGPC is similarly constructed, which is designed from the CARIMA models in the NFN. The NFGPC is most suitable for processes with smooth nonlinearity, such that its full operating range can be partitioned into several local linear operating regions. The proposed NFGPC therefore provides a useful alternative for controlling this class of nonlinear power plants, which are formerly difficult to be controlled using traditional methods.Part 4 为Part3译文：锅炉蒸汽温度模糊神经网络的广义预测控制Xiangjie LIU, Jizhen LIU, Ping GUAN摘要：发电厂是非线性和不确定性的复杂系统。
外文翻译--农业温室大棚智能自动化控制
毕业设计论文外文资料翻译学院：电气学院专业：电气工程及其自动化姓名：学号：外文出处：Agricultural greenhousesgreenhouse intelligent automaticcontrol附件： 1.外文资料翻译译文；2.外文原文。
附件1：外文资料翻译译文农业温室大棚智能自动化控制摘要：历来确定的轨迹到controlgreenhouse农作物生长的问题解决了用约束优化或应用人工智能技术。
已被用作经济利润的最优化研究的主要标准，以获得充足的气候控制设定值，为作物生长。
本文讨论了通过分层控制体系结构由一个高层次的多目标优化方法，要解决这个问题是要找到白天和夜间温度（气候相关的设定值）和电导率的参考轨迹管辖的温室作物生长的问题（ fertirrigation的相关设定值）。
的目标是利润最大化，果实品质，水分利用效率，这些目前正在培育的国际规则。
在过去8年来，获得在工业温室的选择说明结果显示和描述关键词分层农业;系统，过程控制，优化方法;产量优化1。
介绍现代农业是时下在质量和环境影响方面的规定，因此，它是一个自动控制技术的应用已在过去几年增加了很多（[法卡斯，2005和Sigrimis，2000] [Sigrimis 等。
，2001]，[Sigrimis和国王，1999]和Straten等。
，2010]）。
温室生产agrosystem的是一个复杂的物理，化学和生物过程，同时发生，反应不同的响应时间和环境因素的模式，特点是许多相互作用（Challa及Straten，1993年），必须以控制种植者获得最好的结果。
作物生长过程是最重要的，主要是由周围环境的气候变量（光合有效辐射PAR - ，温度，湿度，和内空气中的二氧化碳浓度）的影响，水和化肥，灌溉，虫害和疾病提供的金额，如修剪和处理他人之间的农药和文化的劳动力。
温室是理想的增长，因为它构成一个封闭的环境，气候和fertirrigation变量在可控制的作物。
中英文外文翻译PLC和微处理器定
Introductions of PLC and MCUA PLC is a device that was invented to replace the necessary sequential relay circuits for machine control. The PLC works by looking at its inputs and depending upon their state, turning on/off its outputs .The user enters a program, usually via software or programmer that gives the desired results.PLC are used in many “real world〞applications. If there is industry present, chances are good that there is a PLC present. If you are involved in machining, packaging, material handling, automated assembly or countless other industries, you are probably already using them. If you are not, you are wasting money and time. Almost any application that needs some type of electrical control has need for PLC.For example, let’s assume that when a switch turns on we want to turn a solenoid on for 5 seconds and then turn it off regardless of how long the switch is on for. We can do this with a simple external timer. What if the process also needed to count how many times the switch individually turned on? We need a lot of external counters.As you can see, the bigger the process the more of a need we have for a PLC. We can simply program the PLC to count its inputs and turn the solenoids on for the specified time.We will take a look at what i s considered to be the “top 20〞PLC instructions. It can be safely estimated that with a firm understanding of there instructions one can solve more than 80% of the applications in existence.That‘s right, more than 80%! Of course we’ll learn more than jus t these instructions to help you solve almost ALL your potential PLC applications.The PLC mainly consists of a CPU, memory areas, and appropriate circuits to receive input/output data, as shown in Fig. 19.1 We can actually consider the PLC to be a box full of hundreds or thousands of separate relays, counters, timer and date storage locations. Do these counters, timers, etc. really exist? No, they don’t “physically〞exist but rather they are simulated and can be considered software counters, timers, etc. These internal relays are simulated through bit locations in registers.What does each part do?INPUT RELAYS-(contacts) These are connected to the outside world. They physically exist and receive signals from switches, sensors, etc... Typically they are not relays but rather they are transistors.INTERNAL UTILITY RELAYS-(contacts) These do not receive signals from the outside world nor do they physically exist. They are simulated relays and are what enables a PLC to eliminate external relays. There are also some special relays that are dedicated to performing only one task. Some are always on while some are always off. Some are on only once during power-on and are typically user for initializing data what was stored.COUNTERS These again do not physically exist. They are simulated counters and they can be programmed to count pulses. Typically these counters can count up, down or both up and down. Since there are simulated, they are limited in their counting speed. Some manufacturers also include high-speed counters that are hardware based. We can think of these as physically existing. Most timers these counters can count up, down or up and down.TIMERS These also do not physically exist. They come in many varieties and increments. The most common type is an on-delay type. Other include off-delay and both retentive and non-retentive types. Increments vary from 1ms through 1s.OUTPUT RELAYS-(coil) These are connected to the outside world. They physically exist and send on/off signals to solenoids, lights, etc… They can be transistors, relays, or triacs depending upon the model chosen.DATA STORAGE-Typically there are registers assigned to simply store data. There are usually used as temporary storage for math or data manipulation. They can also typically be user power-up they will still have the same contents as before power war removed. Very convenient and necessary!A PLC works by continually scanning a program. We can think of this scan cycle as consisting of 3 important steps, as shown in Fig. There are typically more than 3 but we can focus on the important parts and not worry about the others. Typically the others are checking the system and updating the current and timer values.Step 1-CHECK INPUT STATUS-First the PLC takes a look at each input to determine if it is on or off. In other words, is the sensor connected to the first input on? How about the second input? How about the third…It records this data into its memory to be used during the next step.Step 2-EXECUTE PROGRAM-Next the PLC executes your program one instruction at a time. Maybe your program said that if the first input was on then it should turn on the first output. Since is already knows which inputs are on/off from the previous step, it will be able to decide whether the first output should be turned onbased on the state of the first input. It will store the execution results for use later during the next step.Step 3-UPDATE OUTPUT STSTUS-Finally the PLC updates the status of outputs. It updates the outputs based on which inputs were on during the first step and the results of executing your program during the second step. Based on the example in step 2 it would now turn on the first output because the first input was on and your program said to turn on the first output when this condition is true.After the third step the PLC goes back to step one and repeats the steps continuously. One scan time is defined as the time is takes to execute the 3 steps listed above. Thus a practical system is controlled to perform specified operations as desired.The AT89S52 is a low-power, high-performance CMOS 8-bit microcontroller with 8Kbytes of in-system programmable Flash memory. The device is manufactured using Atmel’s high-density nonvolatile memory technology and is compatible with the industry-standard 80C51 instruction set and pin-out. The on-chip Flash allows the program memory to be reprogrammed in-system or by a conventional nonvolatile memory programmer. By combining a versatile 8-bit CPU with in-system programmable Flash on a monolithic chip, the Atmel AT89S52 is a powerful microcontroller which provides a highly-flexible and cost-effective solution to many embedded control applications.The AT89S52 provides the following standard features: 8K bytes of Flash, 256 bytes of RAM, 32 I/O lines, Watchdog timer, two data pointers, three 16-bit timer/counters, a six-vector two-level interrupt architecture, a full duplex serial port, on-chip oscillator, and clock circuitry. In addition, the AT89S52 is designed with static logic for operation down to zero frequency and supports two software selectable power saving modes. The Idle Mode stops the CPU while allowing the RAM, timer/counters, serial port, and interrupt system to continue functioning. The Power-down mode saves the RAM contents but freezes the oscillator, disabling all other chip functions until the next interrupt or hardware reset.Port 0 is an 8-bit open drain bidirectional I/O port. As an output port, each pin can sink eight TTL inputs. When is written to port 0 pins, the pins can be used as high-impedance inputs.Port 0 can also be configured to be the multiplexed lowered address/data bus during accesses to external program and data memory. In this mode, P0 has internal pull-ups.Port 0 also receives the code bytes during Flash programming and outputs the code bytes during program verification. External pull-ups are required during program verification.Port 1 is an 8-bit bidirectional I/O port with internal Port 1 output buffers can sink/source four TTL inputs. When 1s are written to Port 1 pins, they are pulled high by the internal pull-ups and can be used as inputs. As inputs, Port 1 pins that are externally being pulled low will source current (I IL) because of the internal pull-ups.In addition, P1.0 and P1.1 can be configured to be the timer/counter 2 external count input (P1.0/T2) and the timer/counter 2 trigger input (P1.1/T2EX).PLC和微处理器简介PLC(可编程逻辑控制器)是极限控制中为代替必要的继电器时序电路而创造的一种设备。
自动化专业外文翻译--Alicia3爬壁机器人的粘着控制
英语原文:Adhesion Control for the Alicia3 Climbing RobotD. Longo and G. Muscat。
Dipartime nto di In geg neria Elettrica Elettr onica e dei Sistemi, Uni versit'a degliStudi di Cata nia, viale A. Doria 6, 95125 Cata nia Italy Abstract. Climbing robots are useful devices that can be adopted in a variety of applicati ons like maintenan ce, buildi ng, in spect ion and safety in the process and con struct ion in dustries.The mai n target of the Alicia3 robot is to in spect non porous vertical wall with any regard for the material of the wall. To meet this target, a pneumatic-like adhesi on for the system has bee n selected. Also the system can move over the surface with a suitable velocity by means of two DC motors and overcomesome obstacle tha nks to a special cup seali ng.This adhesi on tech no logy requires a suitable con troller to improve system reliability. This is because small obstacles passing under the cup and wall irregularityca n vary the value of the internal pressure of the cup putt ing the robot in some ano malous work ing con diti ons. The methodologies used for deriv ing an accuratesystemmodel and controller will be explained and some result will be prese nted in this work.1 IntroductionClimbing robots can be used to inspect vertical walls to search for potential damage or problems on exter nal or internal surface ofabovegro und/un dergro und etrochemical storage tan ks, con crete walls and metallic structures[14]. By using this system ascarrier, it will be possible to con duct anu mber of NDI over the wall by carry ing suitable in strume ntati on [5, 6].The mai n applicati on of the proposed system is the automatic inspectionof the external surface of aboveground petrochemical storage tanks where it is very important to perform periodic in specti ons (rate of corrosi on, risk of air or water polluti on) at differe nt rates, as sta ndardized by the America nPetroleum In stitute [7]. The system can be also adopted to in spect con crete dams.While these kinds of inspections are important to prevent ecological disasters and risks for the people working around the plant, these are very expensive because scaffolding is often required and can be very dangerousFig. 1. Typical operati ng en vir onment and the Alicia3 robot for technicians that have to perform these inspections. Moreover, for safety reasons, plant operations must be stopped and the tank must be emptied, clea ned and ven tilated whe n huma n operators are con duct ing in specti ons. In Fig. 1(a) and 1(b) typical environments for climbing robots are shown. Figure 1c shows the Alicia3 robot prototype while attached to a concrete wall during a system test.2 System DescriptionThe Alicia II system (the basic module for the Alicia3 system) is mainly composed by a cup, an aspirator, two actuated wheels that use two DC motors with encoders and gearboxes and four passive steel balls with clearanee to guara ntee pla in con tact of the cup to the wall. The cup can slide over a wall by means of a special seali ng that allows maintaining a suitable vacuum in side the cup and at the same time creating the right amount of friction with respect system weight and a range of a target wall kind.The structure of the Alicia II module, shown in Fig. 2, currently comprises three concentric PVC rings held together by an aluminums disc. The bigger ring and the alu minums disc have a diameter of 30 cm. The seali ng system is allocated in the first two external rings. Both the two rings and the sealing areFig. 2. Structure of the Alicia II module designed to be easily replaceable, as they wear off while the robot is running. Moreover the sealing allows the robot passing over small obstacles (about 1 cm height) like screws or welding traces. The third ring (the smallest one) is usedas a base for a cylinder in which a centrifugal air aspirator and its electrical motor are mounted. The aspirator is used to depressurize the cup formed by the rings andthe sealing, so the whole robot can adhere to the wall like a sta ndard suct ion cup.The motor/aspirator set is very robust and is capable of working in harsh en vir onmen ts. The total weight of the module is 4 Kg.The Alicia3 robot is made with the three modules linked together by means of two rods and a special rotational joint. By using two pneumatic pistons it is possible to rise and to lower each module to overcome obstacles. Each module can be raised 15 cm with respect to the wall, so obstacles that are 10 —2 cm height, can be easily overcame. The system is designed to be able to stay attached using only two cups while the third, any of the three, is raised up. The total weight of the system is about 20 Kg.3 Electro-Pneumatic System ModelBy using this kind of movement and sealing method, it is possible, due to unexpected small obstacles on the surface, to have some air leakage in the cup. This leakage can cause the internal negative pressure to rise up and in this situation the robot could fall down. On the other side if the internal pressure is too low (high △ p), a very big normal force is applied to the system. As a con seque nee, the frictio n can in crease in such a way to not allow robot movements. This problem can be solved by introducing a control loop to regulate the pressureinside the chamber to a suitable value to sustain the system. The considered open loop system and the most easily accessible system variables has been schematized inFig. 3; in this scheme the first block includes the electrical and the mechanical subsystem and the second block includes the pneumatic subsystem. The used variables are the Motor voltage refere nce (the in put sig nal that fixes the motor power) and the Vacuum level (the n egative pressure in side the chamber).Fig. 3. The ope n loop system con sideredFig. 4.1/0 variable acquisiti on schemeSince it is very difficult to have a reliable analytical model of that system, because of the big number of parameters invoIved, it has been decided to identify a black box dynamic model of the system by using input/output measurements. This model was designed with two purposes: to compute a suitable control strategy and to implement a simulator for tuning the control parameters.An experimental setup was realized, as represented in Fig. 4, by using theDS1102 DSP board from Dspace in order to gen erate and acquire the in put/output variables. Si nee the aspirator is actuated by an AC motor, a power in terface has bee n realized in order to tran slate in power the refere nee signal for the motor coming from a DAC channel of the DS1102 board. The output system variable has been measured with a piezoresistive pressure sensor with a suitable electronic conditioning block and acquired with one an alog in put of the DS1102. The software running on the DSpace DSP board, in this first phase simply generatesan exciting motor voltage reference signal (pseudo ran dom, ramp or step sig nals) and acquires the two an alog in puts with a sampling time of 0.1 s, storing the data in its internal SRAM.Typical Input/Output measurements are represented in Fig. 5 and Fig. 6. In order to obtain better results in system modeling, the relationship between In put and Output n eeds to be con sidered as non-li near. A NARX model has been used is in the form of (1), where f is a non linear function [8, 9].y(k) = f(u(k), u(k - 1), . . . ; y(k - 1), y(k - 2),...) ⑴To implement this kind of non-linearity, some trials have been done using Neuro-Fuzzy and Artificial Neural Network (ANN) methodologies. Once that model has been trained to a suitable mean square error, it has been simulated giving it as in put the real in put measureme ntonly (infin ite step predictor) [8]. So (1) can be modified in order to obtai n (2).勿(k) = f(u(k), u(k - 1),...;欲k - 1),勿(k - 2),...) (2)In (2), 4y is the estimated system output. In order to compare the simulation results,a number of descriptor has been defined and used. Among these are mean error, quadratic mean error and some correlation indexes. A first set of simulation for both methodologies has been done to find out the best I/O regressi on terms choice.3.1 Neuro-Fuzzy IdentificationUsing this kind of methodology, the best model structure was found to be in the form of (3).y(t) = f(u(t), y(t - 1)) (3)Once the best model structure has been found, some trials have been performed modifying the number of membership functions. The best results, comparing the indexes described above, have been obtained with 3 functions and in Fig. 7 the simulation results has been reported. The structure of the Neuro-Fuzzy model is the ANFIS-Suge no [10].3.2 ANN IdentificationUsing this ki nd of methodology, the best model structure was found to be in the form of (4).y(t) = f(u(t), u(t - 1), u(t - 2), y(t - 1), y(t - 2)) (4)A single layer perceptron network has been used. The training algorithm is the sta ndard Leve nbergMarquardt.Once the best model structure has bee n found, some trials have bee n performed modifying the number of hidden neurons. The best results, comparing the indexes described above, have been obtained with 7 hidden neurons and in Fig. 8 the simulatio n results has bee n reported.From a comparison between the two models and their related indexes, it can be see n that the Neuro-Fuzzy model has best approximati on performa nee and use less in put in formati on. In the n ext sect ion, this model will be used as system emulator to tune and test the required regulator.4 Pressure Control AlgorithmOnce a system model has bee n obta in ed, a closed loop con figurati on like that in Fig. 9, has bee n con sidered.The target of the con trol algorithm is to regulate the internal vacuum level to a suitable value (from some trials, it was fixed to about 10 kPa) to susta in the whole system and its payload; the maximum steady state error allowed was fixed to less than 200Pa. Moreover the time constant of the real system (about 10 s) has to be considered. A first simulation trial has been done with a fuzzy controller while duringa second trial a PID controller has been tuned over the system emulator to meet the controller target. All these simulations have bee n performed by using Simuli nk from Mathworks.4.1 Fuzzy ControllerDuring this simulatio n, a fuzzy con troller that uses as in put only the system error has bee n used. This con troller has three membership fun cti on (tria ngular and trapezoidal) and three output crisp membership fun cti ons.The refere nee was set to 10 kPa and the no ise sig nal on the pressure level is a series of steps. In Fig. 10 a plot of the noise, referenee and closed loop pressure sig nal is represe nted[11].4.2 PID ControllerA second simulation has been done tuning a PID controller over the Neuro- Fuzzy system emulator. As the system model is non-linear, trial and error tech nique has bee n used. The con troller has bee n tested in the same con diti on of the fuzzy con troller. From the Fig. 12 it is possible to see that now the closed loop system has little more overshooting (see Fig. 13 for detail) but the same steady state error. It has to be no ted that overshooti ng is higher that the maximum error allowed but is faster with respect the system time constant.5 ConclusionIn this work the Alicia3 climbing robot was presented. Due to its special adhesi on mecha ni sm, a con troller for the vacuum level in side the cup is required. First of all, a system emulator has been designed by using black box identification methodologies. Among all the performed trial, Artificial Neural Networks and Neuro-Fuzzy are the two best models found and the Neuro- Fuzzy one has been selected as system emulator. A set of indexes has been in troduced in order to make a comparis on and to select the best system model. Once a system emulator has bee n become available, some Simuli nk simulati ons have been performed in order to tune a controller. In that case a Fuzzy and a PID controller have been compared. Between the two, the Fuzzy controller works better tha n the PID but this is much simpler in its impleme ntati on and its performances are not so worst; in any case, it is compatibles with system dyn amics.中文原文Alicia3爬壁机器人的粘着控制摘要.爬壁机器人用途广泛，可以在许多不同的环境中应用，如维修、建设、检查安全的过程和建筑业。
电气 自动化 外文文献 外文翻译 英文文献
外文出处：Farhadi, A. (2008). Modeling, simulation, and reduction of conducted electromagnetic interference due to a pwm buck type switching power supply. Harmonics and Quality of Power, 2008. ICHQP 2008. 13th International Conference on, 1 - 6.Modeling, Simulation, and Reduction of Conducted Electromagnetic Interference Due to a PWM Buck Type Switching Power Supply IA. FarhadiAbstract:Undesired generation of radiated or conducted energy in electrical systems is called Electromagnetic Interference (EMI). High speed switching frequency in power electronics converters especially in switching power supplies improves efficiency but leads to EMI. Different kind of conducted interference, EMI regulations and conducted EMI measurement are introduced in this paper. Compliancy with national or international regulation is called Electromagnetic Compatibility (EMC). Power electronic systems producers must regard EMC. Modeling and simulation is the first step of EMC evaluation. EMI simulation results due to a PWM Buck type switching power supply are presented in this paper. To improve EMC, some techniques are introduced and their effectiveness proved by simulation.Index Terms:Conducted, EMC, EMI, LISN, Switching SupplyI. INTRODUCTIONFAST semiconductors make it possible to have high speed and high frequency switching in power electronics []1. High speed switching causes weight and volume reduction of equipment, but some unwanted effects such as radio frequency interference appeared []2. Compliance with electromagnetic compatibility (EMC) regulations is necessary for producers to present their products to the markets. It is important to take EMC aspects already in design phase []3. Modeling and simulation is the most effective tool to analyze EMC consideration before developing the products. A lot of the previous studies concerned the low frequency analysis of power electronics components []4[]5. Different types of power electronics converters are capable to be considered as source of EMI. They could propagate the EMI in both radiated and conducted forms. Line Impedance Stabilization Network (LISN) is required for measurement and calculation of conducted interference level []6. Interference spectrum at the output of LISN is introduced as the EMC evaluation criterion []7[]8. National or international regulations are the references forthe evaluation of equipment in point of view of EMC []7[]8.II. SOURCE, PATH AND VICTIM OF EMIUndesired voltage or current is called interference and their cause is called interference source. In this paper a high-speed switching power supply is the source of interference.Interference propagated by radiation in area around of an interference source or by conduction through common cabling or wiring connections. In this study conducted emission is considered only. Equipment such as computers, receivers, amplifiers, industrial controllers, etc that are exposed to interference corruption are called victims. The common connections of elements, source lines and cabling provide paths for conducted noise or interference. Electromagnetic conducted interference has two components as differential mode and common mode []9.A. Differential mode conducted interferenceThis mode is related to the noise that is imposed between different lines of a test circuit by a noise source. Related current path is shown in Fig. 1 []9. The interference source, path impedances, differential mode current and load impedance are also shown in Fig. 1.B. Common mode conducted interferenceCommon mode noise or interference could appear and impose between the lines, cables or connections and common ground. Any leakage current between load and common ground couldbe modeled by interference voltage source.Fig. 2 demonstrates the common mode interference source, common mode currents Iandcm1 and the related current paths[]9.The power electronics converters perform as noise source Icm2between lines of the supply network. In this study differential mode of conducted interference is particularly important and discussion will be continued considering this mode only.III. ELECTROMAGNETIC COMPATIBILITY REGULATIONS Application of electrical equipment especially static power electronic converters in different equipment is increasing more and more. As mentioned before, power electronics converters are considered as an important source of electromagnetic interference and have corrupting effects on the electric networks []2. High level of pollution resulting from various disturbances reduces the quality of power in electric networks. On the other side some residential, commercial and especially medical consumers are so sensitive to power system disturbances including voltage and frequency variations. The best solution to reduce corruption and improve power quality is complying national or international EMC regulations. CISPR, IEC, FCC and VDE are among the most famous organizations from Europe, USA and Germany who are responsible for determining and publishing the most important EMC regulations. IEC and VDE requirement and limitations on conducted emission are shown in Fig. 3 and Fig. 4 []7[]9.For different groups of consumers different classes of regulations could be complied. Class Afor common consumers and class B with more hard limitations for special consumers are separated in Fig. 3 and Fig. 4. Frequency range of limitation is different for IEC and VDE that are 150 kHz up to 30 MHz and 10 kHz up to 30 MHz respectively. Compliance of regulations is evaluated by comparison of measured or calculated conducted interference level in the mentioned frequency range with the stated requirements in regulations. In united European community compliance of regulation is mandatory and products must have certified label to show covering of requirements []8.IV. ELECTROMAGNETIC CONDUCTED INTERFERENCE MEASUREMENTA. Line Impedance Stabilization Network (LISN)1-Providing a low impedance path to transfer power from source to power electronics converter and load.2-Providing a low impedance path from interference source, here power electronics converter, to measurement port.Variation of LISN impedance versus frequency with the mentioned topology is presented inFig. 7. LISN has stabilized impedance in the range of conducted EMI measurement []7.Variation of level of signal at the output of LISN versus frequency is the spectrum of interference. The electromagnetic compatibility of a system can be evaluated by comparison of its interference spectrum with the standard limitations. The level of signal at the output of LISN in frequency range 10 kHz up to 30 MHz or 150 kHz up to 30 MHz is criterion of compatibility and should be under the standard limitations. In practical situations, the LISN output is connected to a spectrum analyzer and interference measurement is carried out. But for modeling and simulation purposes, the LISN output spectrum is calculated using appropriate software.基于压降型PWM开关电源的建模、仿真和减少传导性电磁干扰摘要：电子设备之中杂乱的辐射或者能量叫做电磁干扰(EMI)。
可编程控制器本科毕业论文中英文翻译材料关于PLC外文翻译
可编程控制器本科毕业论文中英文翻译材料关于PLC外文翻译中文翻译可编程控制器技术可编程序控制器(Programmable Logic Controller，习惯上简称为PLC)是以微处理器为核心的通用工业自动化装置。
是20世纪60年代末在继电器控制系统的基础上开发出来的，它将传统的继电器控制技术与计算机技术和通信技术融为一体，具有结构简单、性能优越、可靠性高、灵活通用、易于编程、使用方便等优点。
具体来说，PLC的特点表现为以下几个方面:?硬件的可靠性高。
PLC专业在工业环境的恶劣条件下应用而设计。
一个设计良好的PLC能置于有很强电噪声、电磁干扰、机械振动、极端温度和湿度很大的环境中。
在硬件设计方面，首先是选用优质器件，再就是采用合理的系统结构，加固、简化安装，使它易于抗振冲击，对印刷电路板的设计、加工和焊接都采取了极为严格的工艺措施，而在电路、结构及工艺上采取了一些独特的方式。
由于PLC 本身具有很高的可靠性，所以在发生故障的部位大多集中在输入/输出的部位以及如传感器件、限位开关、光电开关、电磁阀、电机等外围装置上。
?编程简单，使用方便。
用微机实现自动控制，常使用汇编语言编程，难于掌握，要求使用者具有一定水平的计算机硬件和软件知识。
PLC采用面向控制过程、面向问题的编程方式，与目前微机控制常用的汇编语言相比，虽然在PLC内部增加了解释程序，增加了程序的执行时间，但对大多数的机电控制设备来说，这种损耗是微不足道的。
?接线简单，通用性好。
在电信号匹配的情况下，PLC的接线只需将输入信号的设备(按钮、开关等)与PLC输入端子连接，将接受输出信号执行控制任务的执行元件(接触器、电磁阀)与PLC输出端子连接。
接线简单、工作量少，省去了传统的继电器控制系统的接线和拆线的麻烦。
PLC的编程逻辑提供了能随要求而改变的逻辑关系，这样生产线的自动化过程就能随意改变。
这种性能使PLC具有很高的经济效益。
用于连接现场设备的硬件接口实际上已经设计成为PLC的组成部分，模块化的自诊断接口电路能指出故障，并易于排除故障与替换故障部件，这样的软硬件设计就使现场电气人员与技术人员易于使用。
自动控制毕业论文中英文资料外文翻译--模块化安全铁路信号计算机联锁系统
中文2570字外文文献翻译院、部：电气与信息工程学院学生姓名：指导教师：职称讲师专业：自动化班级： 09级01班完成时间： 2013.06.06出处：Computing, Communication, Control, and Management, 2008. CCCM'08. ISECS International Colloquium on. IEEE, 2008, 1: 538-541Component-based Safety Computer of Railway SignalInterlocking System1 IntroductionSignal Interlocking System is the critical equipment which can guarantee traffic safety and enhance operational efficiency in railway transportation. For a long time, the core control computer adopts in interlocking system is the special customized high-grade safety computer, for example, the SIMIS of Siemens, the EI32 of Nippon Signal, and so on. Along with the rapid development of electronic technology, the customized safety computer is facing severe challenges, for instance, the high development costs, poor usability, weak expansibility and slow technology update. To overcome the flaws of the high-grade special customized computer, the U.S. Department of Defense has put forward the concept：we should adopt commercial standards to replace military norms and standards for meeting consumers’demand [1]. In the meantime, there are several explorations and practices about adopting open system architecture in avionics. The United Stated and Europe have do much research about utilizing cost-effective fault-tolerant computer to replace the dedicated computer in aerospace and other safety-critical fields. In recent years, it is gradually becoming a new trend that the utilization of standardized components in aerospace, industry, transportation and other safety-critical fields.2 Railways signal interlocking system2.1 Functions of signal interlocking systemThe basic function of signal interlocking system is to protect train safety by controlling signal equipments, such as switch points, signals and track units in a station, and it handles routes via a certain interlocking regulation.Since the birth of the railway transportation, signal interlocking system has gone through manual signal, mechanical signal, relay-based interlocking, and the modern computer-based Interlocking System.2.2 Architecture of signal interlocking systemGenerally, the Interlocking System has a hierarchical structure. According to the function of equipments, the system can be divided to the function of equipments; the system can be divided into three layers as shown in figure1.Man-Machine Interface layerInterlocking safety layerImplementation layerOutdoorequiptmentsFigure 1 Architecture of Signal Interlocking System3 Component-based safety computer design3.1 Design strategyThe design concept of component-based safety critical computer is different from that of special customized computer. Our design strategy of SIC is on a base of fault-tolerance and system integration. We separate the SIC into three layers, the standardized component unit layer, safety software layer and the system layer. Different safety functions are allocated for each layer, and the final integration of the three layers ensures the predefined safety integrity level of the whole SIC. The three layers can be described as follows:(1) Component unit layer includes four independent standardized CPU modules. A hardware “SAFETY AND” logic is implemented in this year.(2) Safety software layer mainly utilizes fail-safe strategy and fault-tolerant management. The interlocking safety computing of the whole system adopts two outputs from different CPU, it can mostly ensure the diversity of software to hold with design errors of signal version and remove hidden risks.(3) System layer aims to improve reliability, availability and maintainability by means of redundancy.3.2 Design of hardware fault-tolerant structureAs shown in figure 2, the SIC of four independent component units (C11, C12, C21, C22). The fault-tolerant architecture adopts dual 2 vote 2 (2v2×2) structure, and a kind of high-performance standardized module has been selected as computing unit which adopts Intel X Scale kernel, 533 MHZ.The operation of SIC is based on a dual two-layer data buses. The high bus adopts thestandard Ethernet and TCP/IP communication protocol, and the low bus is Controller Area Network (CAN). C11、C12 and C21、C22 respectively make up of two safety computing components IC1 and IC2, which are of 2v2 structure. And each component has an external dynamic circuit watchdog that is set for computing supervision and switching.Diagnosis terminal C12C21C22&&Watchdog driverFail-safe switch Input modle Output Modle InterfaceConsole C11High bus(Ether NET)Low bus (CAN)Figure 2 Hardware structure of SIC3.3 Standardized component unitAfter component module is made certain, according to the safety-critical requirements of railway signal interlocking system, we have to do a secondary development on the module. The design includes power supply, interfaces and other embedded circuits.The fault-tolerant processing, synchronized computing, and fault diagnosis of SIC mostly depend on the safety software. Here the safety software design method is differing from that of the special computer too. For dedicated computer, the software is often specially designed based on the bare hardware. As restricted by computing ability and application object, a special scheduling program is commonly designed as safety software for the computer, and not a universal operating system. The fault-tolerant processing and fault diagnosis of the dedicated computer are tightly hardware-coupled. However, the safety software for SIC is exoteric and loosely hardware-coupled, and it is based on a standard Linux OS.The safety software is vital element of secondary development. It includes Linux OS adjustment, fail-safe process, fault-tolerance management, and safety interlocking logic. The hierarchy relations between them are shown in Figure 4.Safety Interlock LogicFail-safe processFault-tolerance managementLinux OS adjustmentFigure 4 Safety software hierarchy of SIC3.4 Fault-tolerant model and safety computation3.4.1 Fault-tolerant modelThe Fault-tolerant computation of SIC is of a multilevel model:SIC=F1002D(F2002(S c11,S c12),F2002(S c21,S c22))Firstly, basic computing unit Ci1 adopts one algorithm to complete the S Ci1, and Ci2 finishes the S Ci2via a different algorithm, secondly 2 out of 2 (2oo2) safety computing component of SIC executes 2oo2 calculation and gets F SICi from the calculation results of S Ci1 S Ci2, and thirdly, according the states of watchdog and switch unit block, the result of SIC is gotten via a 1 out of 2 with diagnostics (1oo2D) calculation, which is based on F SIC1 and F SIC2.The flow of calculations is as follows:(1) S ci1=F ci1 (D net1,D net2,D di,D fss)(2) S ci2=F ci2 (D net1,D net2,D di,D fss)(3) F SICi=F2oo2 (S ci1, S ci2 ),(i=1,2)(4) SIC_OutPut=F1oo2D (F SIC1, F SIC2)3.4.2 Safety computationAs interlocking system consists of a fixed set of task, the computational model of SIC is task-based. In general, applications may conform to a time-triggered, event-triggered or mixed computational model. Here the time-triggered mode is selected, tasks are executed cyclically. The consistency of computing states between the two units is the foundation of SIC for ensuring safety and credibility. As SIC works under a loosely coupled mode, it is different from that of dedicated hardware-coupled computer. So a specialized synchronization algorithm is necessary for SIC.SIC can be considered as a multiprocessor distributed system, and its computational model is essentially based on data comparing via high bus communication. First, an analytical approach is used to confirm the worst-case response time of each task. To guarantee the deadline of tasks that communicate across the network, the access time and delay of communication medium is set to a fixed possible value. Moreover, the computational model must meets the real time requirements of railway interlocking system, within the system computing cycle, we set many check points P i(i=1,2,... n) , which are small enough for synchronization, and computation result voting is executed at each point. The safetycomputation flow of SIC is shown in Figure 5.S t a r tS t a r t０τ１τ２τ１Ｐ２Ｐ０τ１τ２τ１Ｐ２Ｐ０Ｔ０ＴＣ1i Ｃi 21Ｔ2Ｔ1Ｔ2Ｔ…………………n+1τn+1τn Ｐn Ｐn τn τclockclockS a f e t y f u n c t i o n s T a s k s o f i n t e r l o c k i n g l o g i c i :p:c h e c k p o i n t I n i t i a l i z e S y n c h r o n i z a t i o n G u a r a n t e e S y n c h r o n o u s T i m e t r i g g e rFigure 5 Safety computational model of SIC4. Hardware safety integrity level evaluation4.1 Safety IntegrityAs an authoritative international standard for safety-related system, IEC 61508 presents a definition of safety integrity: probability of a safety-related system satisfactorily performing the required safety functions under all the stated conditions within a stated period of time. In IEC 61508, there are four levels of safety integrity are prescribe, SIL1～SIL4. The SIL1 is the lowest, and SIL4 highest.According to IEC 61508, the SIC belongs to safety-related systems in high demand or continuous mode of operation. The SIL of SIC can be evaluated via the probability of dangerous per hour. The provision of SIL about such system in IEC 61508, see table 1.Table 1-Safety Integrity levels: target failure measures for a safety function operating in high demand orcontinuous mode of operationSafety Integrity levelHigh demand or continuous mode of Operation (Probability of a dangerous Failure per hour)4 ≥10-9 to ＜10-83 ≥10-8 to ＜10-72 ≥10-7 to ＜10-61 ≥10-6 to ＜10-54.2 Reliability block diagram of SICAfter analyzing the structure and working principle of the SIC, we get the bock diagram of reliability, as figure 6.2002200220022002NET1NET2NET1NET2λ=1×10-7DC=99%Voting=1002D λ=1×10-7DC=99%Voting=1002D λ=1×10Β=2%βD =1%DC=99%Voting=1002D High busLogic subsystem Low busFigure 6 Block diagram of SIC reliability5. ConclusionsIn this paper, we proposed an available standardized component-based computer SIC. Railway signal interlocking is a fail-safe system with a required probability of less than 10-9 safety critical failures per hour. In order to meet the critical constraints, fault-tolerant architecture and safety tactics are used in SIC. Although the computational model and implementation techniques are rather complex, the philosophy of SIC provides a cheerful prospect to safety critical applications, it renders in a simpler style of hardware, furthermore, it can shorten development cycle and reduce cost. SIC has been put into practical application, and high performance of reliability and safety has been proven.模块化安全铁路信号计算机联锁系统1概述信号联锁系统是保证交通安全、提高铁路运输效率的关键设备。
自动化专业常用英语词汇
自动化专业常用英语词汇引言概述：自动化专业是现代工程技术领域中的重要学科，涵盖了自动控制、机器人技术、电气工程等多个方面。
在学习和工作中，掌握一些常用的英语词汇对于自动化专业的学生和从业人员来说非常重要。
本文将介绍自动化专业常用的英语词汇，并按照一、二、三、四、五五个部份进行详细阐述。
一、自动控制（Automatic Control）1.1 控制系统（Control System）：用于监测、测量和调节工业过程的设备和技术。
1.2 反馈控制（Feedback Control）：通过监测输出信号并与期望值进行比较，调整输入信号以实现稳定控制的方法。
1.3 开环控制（Open-loop Control）：无需反馈信号，通过预设的输入信号来控制系统。
二、机器人技术（Robotics）2.1 机器人（Robot）：一种能够自动执行任务的复杂机械设备。
2.2 传感器（Sensor）：用于感知环境和获取信息的装置，如视觉传感器、力传感器等。
2.3 人机交互（Human-Machine Interaction）：机器人与人类之间的信息交流和合作。
三、电气工程（Electrical Engineering）3.1 电路（Circuit）：电气元件按照一定连接方式形成的路径，用于传输电流。
3.2 机电（Motor）：将电能转换为机械能的装置，如直流机电、交流机电等。
3.3 电力系统（Power System）：用于生成、传输和分配电能的设备和网络。
四、工业自动化（Industrial Automation）4.1 自动化生产线（Automated Production Line）：利用计算机控制和机械设备实现产品自动创造的生产线。
4.2 传输系统（Conveyor System）：用于自动输送物料和产品的系统，如传送带、输送机等。
4.3 过程控制（Process Control）：对工业过程中的物理和化学变化进行监测和调节的技术。
机械类数控车床外文翻译外文文献英文文献数控
数控加工中心技术开展趋势与对策原文来源：Zhao Chang-ming Liu Wang-ju(C Machining Processand equipment,2002,China)一、摘要Equip the engineering level, level of determining the whole national economy of the modernized degree and modernized degree of industry, numerical control technology is it develop new developing new high-tech industry and most advanced industry to equip (such as information technology and his industry, biotechnology and his industry, aviation, spaceflight, etc. national defense industry) last technology and getting more basic most equipment.Numerical control technology is the technology controlled to mechanical movement and working course with digital information, integrated products of electromechanics that the numerical control equipment is the new technology represented by numerical control technology forms to the manufacture industry of the tradition and infiltration of the new developing manufacturing industry,Keywords:Numerical ControlTechnology, E quipment,industry二、译文数控技术和装备开展趋势与对策装备工业的技术水平和现代化程度决定着整个国民经济的水平和现代化程度，数控技术与装备是开展新兴高新技术产业和尖端工业〔如信息技术与其产业、生物技术与其产业、航空、航天等国防工业产业〕的使能技术和最根本的装备。
智能控制系统毕业论文中英文资料对照外文翻译文献
智能控制系统中英文资料对照外文翻译文献附录一：外文摘要The development and application of Intelligence controlsystemModern electronic products change rapidly is increasingly profound impact on people's lives, to people's life and working way to bring more convenience to our daily lives, all aspects of electronic products in the shadow, single chip as one of the most important applications, in many ways it has the inestimable role. Intelligent control is a single chip, intelligent control of applications and prospects are very broad, the use of modern technology tools to develop an intelligent, relatively complete functional software to achieve intelligent control system has become an imminent task. Especially in today with MCU based intelligent control technology in the era, to establish their own practical control system has a far-reaching significance so well on the subject later more fully understanding of SCM are of great help to.The so-called intelligent monitoring technology is that:" the automatic analysis and processing of the information of the monitored device". If the monitored object as one's field of vision, and intelligent monitoring equipment can be regarded as the human brain. Intelligent monitoring with the aid of computer data processing capacity of the powerful, to get information in the mass data to carry on the analysis, some filtering of irrelevant information, only provide some key information. Intelligent control to digital, intelligent basis, timely detection system in the abnormal condition, and can be the fastest and best way to sound the alarm and provide usefulinformation, which can more effectively assist the security personnel to deal with the crisis, and minimize the damage and loss, it has great practical significance, some risk homework, or artificial unable to complete the operation, can be used to realize intelligent device, which solves a lot of artificial can not solve the problem, I think, with the development of the society, intelligent load in all aspects of social life play an important reuse.Single chip microcomputer as the core of control and monitoring systems, the system structure, design thought, design method and the traditional control system has essential distinction. In the traditional control or monitoring system, control or monitoring parameters of circuit, through the mechanical device directly to the monitored parameters to regulate and control, in the single-chip microcomputer as the core of the control system, the control parameters and controlled parameters are not directly change, but the control parameter is transformed into a digital signal input to the microcontroller, the microcontroller according to its output signal to control the controlled object, as intelligent load monitoring test, is the use of single-chip I / O port output signal of relay control, then the load to control or monitor, thus similar to any one single chip control system structure, often simplified to input part, an output part and an electronic control unit ( ECU )Intelligent monitoring system design principle function as follows: the power supply module is 0~220V AC voltage into a0 ~ 5V DC low voltage, as each module to provide normal working voltage, another set of ADC module work limit voltage of 5V, if the input voltage is greater than 5V, it can not work normally ( but the design is provided for the load voltage in the 0~ 5V, so it will not be considered ), at the same time transformer on load current is sampled on the accused, the load current into a voltage signal, and then through the current - voltage conversion, and passes through the bridge rectification into stable voltage value, will realize the load the current value is converted to a single chip can handle0 ~ 5V voltage value, then the D2diode cutoff, power supply module only plays the role of power supply. Signal to the analog-to-digital conversion module, through quantization, coding, the analog voltage value into8bits of the digital voltage value, repeatedly to the analog voltage16AD conversion, and the16the digital voltage value and, to calculate the average value, the average value through a data bus to send AT89C51P0, accepted AT89C51 read, AT89C51will read the digital signal and software setting load normal working voltage reference range [VMIN, VMAX] compared with the reference voltage range, if not consistent, then the P1.0 output low level, close the relay, cut off the load on the fault source, to stop its sampling, while P1.1 output high level fault light, i.e., P1.3 output low level, namely normal lights. The relay is disconnected after about 2minutes, theAT89C51P1.0outputs high level ( software design), automatic closing relay, then to load the current regular sampling, AD conversion, to accept the AT89C51read, comparison, if consistent, then the P1.1 output low level, namely fault lights out, while P1.3 output high level, i.e. normal lamp ( software set ); if you are still inconsistent, then the need to manually switch S1toss to" repair" the slip, disconnect the relay control, load adjusting the resistance value is: the load detection and repair, and then close the S1repeatedly to the load current sampling, until the normal lamp bright, repeated this process, constantly on the load testing to ensure the load problems timely repair, make it work.In the intelligent load monitoring system, using the monolithic integrated circuit to the load ( voltage too high or too small ) intelligent detection and control, is achieved by controlling the relay and transformer sampling to achieve, in fact direct control of single-chip is the working state of the relay and the alarm circuit working state, the system should achieve technical features of this thesis are as follows (1) according to the load current changes to control relays, the control parameter is the load current, is the control parameter is the relay switch on-off and led the state; (2) the set current reference voltage range ( load normal working voltage range ), by AT89C51 chip the design of the software section, provide a basis for comparison; (3) the use of single-chip microcomputer to control the light-emitting diode to display the current state of change ( normal / fault / repair ); specific summary: Transformer on load current is sampled, a current / voltage converter, filter, regulator, through the analog-digital conversion, to accept the AT89C51chip to read, AT89C51 to read data is compared with the reference voltage, if normal, the normal light, the output port P.0high level, the relay is closed, is provided to the load voltage fault light; otherwise, P1.0 output low level, The disconnecting relay to disconnect the load, the voltage on the sampling, stop. Two minutes after closing relay, timing sampling.System through the expansion of improved, can be used for temperature alarm circuit, alarm circuit, traffic monitoring, can also be used to monitor a system works, in the intelligent high-speed development today, the use of modern technology tools, the development of an intelligent, function relatively complete software to realize intelligent control system, has become an imminent task, establish their own practical control system has a far-reaching significance. Micro controller in the industry design and application, no industry like intelligent automation and control field develop so fast. Since China and the Asian region the main manufacturing plant intelligence to improve the degree of automation, new technology to improve efficiency, have important influence on the product cost. Although the centralized control can be improved in any particular manufacturing process of the overall visual, but not for those response and processingdelay caused by fault of some key application.Intelligent control technology as computer technology is an important technology, widely used in industrial control, intelligent control, instrument, household appliances, electronic toys and other fields, it has small, multiple functions, low price, convenient use, the advantages of a flexible system design. Therefore, more and more engineering staff of all ages, so this graduate design is of great significance to the design of various things, I have great interest in design, this has brought me a lot of things, let me from unsuspectingly to have a clear train of thought, since both design something, I will be there a how to design thinking, this is very important, I think this job will give me a lot of valuable things.中文翻译：智能控制系统的开发应用现代社会电子产品日新月异正在越来越深远的影响着人们的生活，给人们的生活和工作方式带来越来越大的方便，我们的日常生活各个方面都有电子产品的影子，单片机作为其中一个最重要的应用，在很多方面都有着不可估量的作用。
毕业设计毕业论文电气工程及其自动化外文翻译中英文对照
毕业设计毕业论文电气工程及其自动化外文翻译中英文对照电气工程及其自动化外文翻译中英文对照一、引言电气工程及其自动化是一门涉及电力系统、电子技术、自动控制和信息技术等领域的综合学科。
本文将翻译一篇关于电气工程及其自动化的外文文献，并提供中英文对照。
二、文献翻译原文标题：Electric Engineering and Its Automation作者：John Smith出版日期：2020年摘要：本文介绍了电气工程及其自动化的基本概念和发展趋势。
首先，介绍了电气工程的定义和范围。
其次，探讨了电气工程在能源领域的应用，包括电力系统的设计和运行。
然后，介绍了电气工程在电子技术领域的重要性，包括电子设备的设计和制造。
最后，讨论了电气工程与自动控制和信息技术的结合，以及其在工业自动化和智能化领域的应用。
1. 介绍电气工程是一门研究电力系统和电子技术的学科，涉及发电、输电、配电和用电等方面。
电气工程的发展与电力工业的发展密切相关。
随着电力需求的增长和电子技术的进步，电气工程的重要性日益凸显。
2. 电气工程在能源领域的应用电气工程在能源领域的应用主要包括电力系统的设计和运行。
电力系统是由发电厂、输电线路、变电站和配电网络等组成的。
电气工程师负责设计和维护这些设施，以确保电力的可靠供应。
3. 电气工程在电子技术领域的重要性电气工程在电子技术领域的重要性体现在电子设备的设计和制造上。
电子设备包括电脑、手机、电视等消费电子产品，以及工业自动化设备等。
电气工程师需要掌握电子电路设计和数字信号处理等技术，以开发出高性能的电子设备。
4. 电气工程与自动控制和信息技术的结合电气工程与自动控制和信息技术的结合是电气工程及其自动化的核心内容。
自动控制技术可以应用于电力系统的运行和电子设备的控制，以提高系统的稳定性和效率。
信息技术则可以用于数据采集、处理和传输，实现对电力系统和电子设备的远程监控和管理。
5. 电气工程在工业自动化和智能化领域的应用电气工程在工业自动化和智能化领域的应用越来越广泛。
外文翻译资料---电子时钟设计
外文翻译资料---电子时钟设计___。
using digital tubes for high-brightness displays。
offers intuitive and intelligent ns。
and is ___ design for a n electronic clock。
using a single-chip puter (AT89C52) as the core。
The clock features a display composed of seven figures。
showing the week。
hour。
minute。
and second。
It can also switch to year。
month。
and day display modes。
and includes music playback and alarm ___。
it ___.The clock circuit is the computer's core。
___.Since its n。
the clock has ___'s lives。
especially in this eraof efficiency。
It is widely used in human n。
living。
learning。
and other ___。
over time。
people's requirements for the clock have increased。
They not only demand higher n but also more ns。
The clock is no longer just a tool used to display time。
It ___ as alarm clock。
calendar display。
temperature measurement。
电子商务外文文献
电子商务外文文献Title: E-commerce: A Review of the Literature and Perspectives for Future ResearchE-commerce, or electronic commerce, has become a fundamental aspect of business and economic activity in the globalized digital age. The交易研究领域的一个重要组成部分。
在这个日益数字化的时代，电子商务已经成为全球商业和经济活动的一个重要组成部分。
本文旨在回顾和分析电子商务领域的研究现状，探讨未来可能的研究方向和挑战。
The literature on e-commerce has been extensive, covering a range of topics from online retailing to global supply chain management. The Journal of Electronic Commerce in Organizations (JECO) and Journal of Electronic Commerce Research (JECR) are two of the leading journals in the field, publishing high-quality research on various aspects ofe-commerce. Additionally, several books and conference proceedings provide valuable insights into the development and evolution of e-commerce.E-commerce research has examined the impact of technology on business processes, explored innovative business models, andanalyzed the role of e-commerce in global trade and development. The literature has addressed a range of important issues, including security and privacy, electronic payment systems, and the impact of social media on e-commerce.Despite the significant progress made in e-commerce research, several areas for future exploration remn. These include the development of new e-commerce technologies, such as blockchn and artificial intelligence, and their potential impact on global trade and supply chns. Additionally, research on the role of e-commerce in sustnable development, particularly in terms of environmental sustnability and social inclusivity, represents an important area for future investigation.In conclusion, e-commerce has become a fundamental aspect of business and economic activity in the digital age. The literature on e-commerce has provided valuable insights into its development and evolution, but there remn several areas for future exploration. Future research should address these unexplored areas and contribute to the development ofe-commerce as a transformative force in global trade and development.商学院电子商务外文文献Title: E-commerce in Business Schools: A Critical Analysis of Curriculum, Teaching Methods, and Future TrendsThe rise of e-commerce in recent years has revolutionized business education, with business schools across the globe scrambling to keep up with the latest trends and prepare students for the digital economy. This article delves into the world of e-commerce education in business schools, exploring curriculum, teaching methods, and predicting future trends. E-commerce has become an integral part of modern business, and business schools are responding to this trend by incorporating e-commerce courses into their curriculum. The primary objective of these courses is to provide students with a comprehensive understanding of the e-commerce industry, including the latest trends, tools, and techniques. In addition to fundamental topics such as online marketing and web design, today's e-commerce courses also cover more specialized topics such as cloud computing, big data analysis, and social media marketing.Business schools are adopting a variety of teaching methods to impart knowledge on e-commerce, ranging from traditional classroom lectures to more innovative hands-onbs and simulations. These experiential learning opportunities allow students to gain practical experience in real-world settings, providing them with a deeper understanding of the dynamics and challenges of the e-commerce industry.With the continuous evolution of the internet and e-commerce landscape, it is essential to track and predict future trends in this field. Business schools are playing a crucial role in this regard by staying abreast of industry developments and incorporating relevant content into their courses. The trend towards more personalized and interactive learning experiences is likely to continue, with business schools tloring their teaching methods to suit the needs of individual students. Additionally, the integration of technology into every aspect of business will continue to drive changes in e-commerce education, with an increasing focus on areas such as cybersecurity and artificial intelligence.In conclusion, business schools have responded to the rise of e-commerce with a comprehensive approach that includes updating curriculum, adopting innovative teaching methods, and predicting future trends. However, there are still challengesahead, such as keeping up with the rapidly changing landscape and providing all students with equal opportunities to access e-commerce education. By continuing to adapt and innovate, business schools can help shape a brighter future fore-commerce and prepare students to thrive in the digital economy.电子商务外文翻译文献电子商务的发展及其影响：外文翻译文献随着全球互联网的迅速普及，电子商务在全球范围内得到了前所未有的发展。
外文翻译--可编程逻辑控制器
毕业设计(论文)外文资料翻译系部：机械工程系专业：机械工程及自动化姓名：学号：外文出处：Process automationinstrumentation附件： 1.外文资料翻译译文；2.外文原文。
注：请将该封面与附件装订成册。
附件1：外文资料翻译译文可编程逻辑控制器1 PLC介绍PLCs（可编程逻辑控制器）是用于各种自动控制系统和过程的可控网络集线器。
他们包含多个输入输出，输入输出是用晶体管和其它电路，模拟开关和继电器来控制设备的。
PLCs用软件接口，标准计算器接口，专门的语言和网络设备编程。
可编程逻辑控制器I/O通道规则包括所有的输入触点和输出触点，扩展能力和最大数量的通道。
触点数量是输入点和输出点的总和。
PLCs可以指定这些值的任何可能的组合。
扩展单元可以被堆栈或互相连接来增加总的控制能力。
最大数量的通道是在一个扩展系统中输入和输出通道的最大总数量。
PLCs系统规则包括扫描时间，指令数量，数据存储和程序存储。
扫描时间是 PLC需要的用来检测输入输出模块的时间。
指令是用于PLC软件（例如数学运算）的标准操作。
数据存储是存储数据的能力。
程序存储是控制软件的能力。
用于可编程逻辑控制器的输入设备包括DC，AC，中间继电器，热电偶，RTD，频率或脉冲，晶体管和中断信号输入；输出设备包括DC，AC，继电器，中间继电器，频率或脉冲，晶体管，三端双向可控硅开关元件；PLC的编程设备包括控制面板，手柄和计算机。
可编程逻辑控制器用各种软件编程语言来控制。
这些语言包括IEC61131-3，顺序执行表（SFC），动作方块图（FBD），梯形图（LD），结构文本（ST），指令序列（IL），继电器梯形图（RIL），流程图，C语言和Basic语言。
IEC61131-3编程环境能支持五种语言，用国际标准加以规范，分别为SFC，FBD，LD，ST和IL。
这便允许了多卖主兼容性和多种语言编程。
SFC是一种图表语言，它提供了编程顺序的配合，就能支持顺序选择和并列选择，二者择其一即可。
仓储物流外文文献翻译中英文原文及译文2023-2023
仓储物流外文文献翻译中英文原文及译文2023-2023原文1：The Current Trends in Warehouse Management and LogisticsWarehouse management is an essential component of any supply chain and plays a crucial role in the overall efficiency and effectiveness of logistics operations. With the rapid advancement of technology and changing customer demands, the field of warehouse management and logistics has seen several trends emerge in recent years.One significant trend is the increasing adoption of automation and robotics in warehouse operations. Automated systems such as conveyor belts, robotic pickers, and driverless vehicles have revolutionized the way warehouses function. These technologies not only improve accuracy and speed but also reduce labor costs and increase safety.Another trend is the implementation of real-time tracking and visibility systems. Through the use of RFID (radio-frequency identification) tags and GPS (global positioning system) technology, warehouse managers can monitor the movement of goods throughout the entire supply chain. This level of visibility enables better inventory management, reduces stockouts, and improves customer satisfaction.Additionally, there is a growing focus on sustainability in warehouse management and logistics. Many companies are implementing environmentally friendly practices such as energy-efficient lighting, recycling programs, and alternativetransportation methods. These initiatives not only contribute to reducing carbon emissions but also result in cost savings and improved brand image.Furthermore, artificial intelligence (AI) and machine learning have become integral parts of warehouse management. AI-powered systems can analyze large volumes of data to optimize inventory levels, forecast demand accurately, and improve operational efficiency. Machine learning algorithms can also identify patterns and anomalies, enabling proactive maintenance and minimizing downtime.In conclusion, warehouse management and logistics are continuously evolving fields, driven by technological advancements and changing market demands. The trends discussed in this article highlight the importance of adopting innovative solutions to enhance efficiency, visibility, sustainability, and overall performance in warehouse operations.译文1：仓储物流管理的当前趋势仓储物流管理是任何供应链的重要组成部分，并在物流运营的整体效率和效力中发挥着至关重要的作用。
自动化专业外文翻译--自动控制的发展历史
HISTORY OF AUTOMATIC CONTROLFeedback control is the basic mechanism by which systems, whether mechanical, electrical, or biological, maintain their equilibrium or homeostasis. In the higher life forms, the conditions under which life can continue are quite narrow. A change in body temperature of half a degree is generally a sign of illness. The homeostasis of the body is maintained through the use of feedback control [Wiener 1948]. A primary contribution of CR Darwin during the last century was the theory that feedback over long time periods is responsible for the evolution of species. In 1931 V. V olterra explained the balance between two populations of fish in a closed pond using the theory of feedback.The use of feedback to control a system has a fascinating history .The first applications of feedback control appeared in the development of float regulator mechanisms in Greece in the period 300 to1B.C. The water clock of ktesibios used a float regulator. An oil lamp devised by Philon in approximately 250 B.C .Used a float regular in an oil lamp for maintaining a constant levels of fuel oil .Heron of Alexandria, who lived in the first century A.D, published a book entitled Pneumatica, which outlined several forms of water-level mechanisms using floating regulators The first feedback system to be invented in modern Europe was the temperature regulator of CornelisDrebb(1572-1633) of Holland Dennis Papin invented the first pressure regulator for steam boilers In 1681.Papin’s pressure regulator was a form of safety Regulator Similar to a pressure-cooker valve.Feedback control may be defined as the use of difference signals, determined by comparing the actual values of system variables to their desired values, as a means of controlling a system. An everyday example of a feedback control system is an automobile speed control, which uses the difference between the actual and the desired speed to vary the fuel flow rate. Since the system output is used to regulate its input, such a device is said to be a closed-loop control system.The first historical feedback system, claimed by Russia, is the water-level float regulator said to have been invented by I. Polzunov in 1765. The float detects the water level and controls the valve that covers the water inlet in the boiler.There have been many developments in automatic control theory during recent years. It is difficult to provide an impartial analysis of an area while it is still developing; however, looking back on the progress of feedback control theory it is by now possible to distinguish some main trends and point out some key advances. Feedback control is an engineering discipline. As such, its progress is closely tied to the practical problems that needed to be solved during any phase of human history. The key developments in the history of mankind that affected the progress of feedback control were:1、The preoccupation of the Greeks and Arabs with keeping accurate track of time.。
自动化控制工程外文翻译外文文献英文文献
Team-Centered Perspective for Adaptive Automation DesignLawrence J.PrinzelLangley Research Center, Hampton, VirginiaAbstractAutomation represents a very active area of human factors research. Thejournal, Human Factors, published a special issue on automation in 1985.Since then, hundreds of scientific studies have been published examiningthe nature of automation and its interaction with human performance.However, despite a dramatic increase in research investigating humanfactors issues in aviation automation, there remain areas that need furtherexploration. This NASA Technical Memorandum describes a new area ofIt discussesautomation design and research, called “adaptive automation.” the concepts and outlines the human factors issues associated with the newmethod of adaptive function allocation. The primary focus is onhuman-centered design, and specifically on ensuring that adaptiveautomation is from a team-centered perspective. The document showsthat adaptive automation has many human factors issues common totraditional automation design. Much like the introduction of other new technologies and paradigm shifts, adaptive automation presents an opportunity to remediate current problems but poses new ones forhuman-automation interaction in aerospace operations. The review here isintended to communicate the philosophical perspective and direction ofadaptive automation research conducted under the Aerospace OperationsSystems (AOS), Physiological and Psychological Stressors and Factors (PPSF)project.Key words:Adaptive Automation; Human-Centered Design; Automation;Human FactorsIntroduction"During the 1970s and early 1980s...the concept of automating as much as possible was considered appropriate. The expected benefit was a reduction inpilot workload and increased safety...Although many of these benefits have beenrealized, serious questions have arisen and incidents/accidents that have occurredwhich question the underlying assumptions that a maximum availableautomation is ALWAYS appropriate or that we understand how to designautomated systems so that they are fully compatible with the capabilities andlimitations of the humans in the system."---- ATA, 1989The Air Transport Association of America (ATA) Flight Systems Integration Committee(1989) made the above statement in response to the proliferation of automation in aviation. They noted that technology improvements, such as the ground proximity warning system, have had dramatic benefits; others, such as the electronic library system, offer marginal benefits at best. Such observations have led many in the human factors community, most notably Charles Billings (1991; 1997) of NASA, to assert that automation should be approached from a "human-centered design" perspective.The period from 1970 to the present was marked by an increase in the use of electronic display units (EDUs); a period that Billings (1997) calls "information" and “management automation." The increased use of altitude, heading, power, and navigation displays; alerting and warning systems, such as the traffic alert and collision avoidance system (TCAS) and ground proximity warning system (GPWS; E-GPWS; TAWS); flight management systems (FMS) and flight guidance (e.g., autopilots; autothrottles) have "been accompanied by certain costs, including an increased cognitive burden on pilots, new information requirements that have required additional training, and more complex, tightly coupled, less observable systems" (Billings, 1997). As a result, human factors research in aviation has focused on the effects of information and management automation. The issues of interest include over-reliance on automation, "clumsy" automation (e.g., Wiener, 1989), digital versus analog control, skill degradation, crew coordination, and data overload (e.g., Billings, 1997). Furthermore, research has also been directed toward situational awareness (mode & state awareness; Endsley, 1994; Woods & Sarter, 1991) associated with complexity, coupling, autonomy, and inadequate feedback. Finally, human factors research has introduced new automation concepts that will need to be integrated into the existing suite of aviationautomation.Clearly, the human factors issues of automation have significant implications for safetyin aviation. However, what exactly do we mean by automation? The way we choose to define automation has considerable meaning for how we see the human role in modern aerospace s ystems. The next section considers the concept of automation, followed by an examination of human factors issues of human-automation interaction in aviation. Next, a potential remedy to the problems raised is described, called adaptive automation. Finally, the human-centered design philosophy is discussed and proposals are made for how the philosophy can be applied to this advanced form of automation. The perspective is considered in terms of the Physiological /Psychological Stressors & Factors project and directions for research on adaptive automation.Automation in Modern AviationDefinition.Automation refers to "...systems or methods in which many of the processes of production are automatically performed or controlled by autonomous machines or electronic devices" (Parsons, 1985). Automation is a tool, or resource, that the human operator can use to perform some task that would be difficult or impossible without machine aiding (Billings, 1997). Therefore, automation can be thought of as a process of substituting the activity of some device or machine for some human activity; or it can be thought of as a state of technological development (Parsons, 1985). However, some people (e.g., Woods, 1996) have questioned whether automation should be viewed as a substitution of one agent for another (see "apparent simplicity, real complexity" below). Nevertheless, the presence of automation has pervaded almost every aspect of modern lives. From the wheel to the modern jet aircraft, humans have sought to improve the quality of life. We have built machines and systems that not only make work easier, more efficient, and safe, but also give us more leisure time. The advent of automation has further enabled us to achieve this end. With automation, machines can now perform many of the activities that we once had to do. Our automobile transmission will shift gears for us. Our airplanes will fly themselves for us. All we have to dois turn the machine on and off. It has even been suggested that one day there may not be aaccidents resulting from need for us to do even that. However, the increase in “cognitive” faulty human-automation interaction have led many in the human factors community to conclude that such a statement may be premature.Automation Accidents. A number of aviation accidents and incidents have been directly attributed to automation. Examples of such in aviation mishaps include (from Billings, 1997):DC-10 landing in control wheel steering A330 accident at ToulouseB-747 upset over Pacific DC-10 overrun at JFK, New YorkB-747 uncommandedroll,Nakina,Ont. A320 accident at Mulhouse-HabsheimA320 accident at Strasbourg A300 accident at NagoyaB-757 accident at Cali, Columbia A320 accident at BangaloreA320 landing at Hong Kong B-737 wet runway overrunsA320 overrun at Warsaw B-757 climbout at ManchesterA310 approach at Orly DC-9 wind shear at CharlotteBillings (1997) notes that each of these accidents has a different etiology, and that human factors investigation of causes show the matter to be complex. However, what is clear is that the percentage of accident causes has fundamentally shifted from machine-caused to human-caused (estimations of 60-80% due to human error) etiologies, and the shift is attributable to the change in types of automation that have evolved in aviation.Types of AutomationThere are a number of different types of automation and the descriptions of them vary considerably. Billings (1997) offers the following types of automation:?Open-Loop Mechanical or Electronic Control.Automation is controlled by gravity or spring motors driving gears and cams that allow continous and repetitive motion. Positioning, forcing, and timing were dictated by the mechanism and environmental factors (e.g., wind). The automation of factories during the Industrial Revolution would represent this type of automation.?Classic Linear Feedback Control.Automation is controlled as a function of differences between a reference setting of desired output and the actual output. Changes a re made to system parameters to re-set the automation to conformance. An example of this type of automation would be flyball governor on the steam engine. What engineers call conventional proportional-integral-derivative (PID) control would also fit in this category of automation.?Optimal Control. A computer-based model of controlled processes i s driven by the same control inputs as that used to control the automated process. T he model output is used to project future states and is thus used to determine the next control input. A "Kalman filtering" approach is used to estimate the system state to determine what the best control input should be.?Adaptive Control. This type of automation actually represents a number of approaches to controlling automation, but usually stands for automation that changes dynamically in response to a change in state. Examples include the use of "crisp" and "fuzzy" controllers, neural networks, dynamic control, and many other nonlinear methods.Levels of AutomationIn addition to “types ” of automation, we can also conceptualize different “levels ” of automation control that the operator can have. A number of taxonomies have been put forth, but perhaps the best known is the one proposed by Tom Sheridan of Massachusetts Institute of Technology (MIT). Sheridan (1987) listed 10 levels of automation control:1. The computer offers no assistance, the human must do it all2. The computer offers a complete set of action alternatives3. The computer narrows the selection down to a few4. The computer suggests a selection, and5. Executes that suggestion if the human approves, or6. Allows the human a restricted time to veto before automatic execution, or7. Executes automatically, then necessarily informs the human, or8. Informs the human after execution only if he asks, or9. Informs the human after execution if it, the computer, decides to10. The computer decides everything and acts autonomously, ignoring the humanThe list covers the automation gamut from fully manual to fully automatic. Although different researchers define adaptive automation differently across these levels, the consensus is that adaptive automation can represent anything from Level 3 to Level 9. However, what makes adaptive automation different is the philosophy of the approach taken to initiate adaptive function allocation and how such an approach may address t he impact of current automation technology.Impact of Automation TechnologyAdvantages of Automation . Wiener (1980; 1989) noted a number of advantages to automating human-machine systems. These include increased capacity and productivity, reduction of small errors, reduction of manual workload and mental fatigue, relief from routine operations, more precise handling of routine operations, economical use of machines, and decrease of performance variation due to individual differences. Wiener and Curry (1980) listed eight reasons for the increase in flight-deck automation: (a) Increase in available technology, such as FMS, Ground Proximity Warning System (GPWS), Traffic Alert andCollision Avoidance System (TCAS), etc.; (b) concern for safety; (c) economy, maintenance, and reliability; (d) workload reduction and two-pilot transport aircraft certification; (e) flight maneuvers and navigation precision; (f) display flexibility; (g) economy of cockpit space; and (h) special requirements for military missions.Disadvantages o f Automation. Automation also has a number of disadvantages that have been noted. Automation increases the burdens and complexities for those responsible for operating, troubleshooting, and managing systems. Woods (1996) stated that automation is "...a wrapped package -- a package that consists of many different dimensions bundled together as a hardware/software system. When new automated systems are introduced into a field of practice, change is precipitated along multiple dimensions." As Woods (1996) noted, some of these changes include: ( a) adds to or changes the task, such as device setup and initialization, configuration control, and operating sequences; (b) changes cognitive demands, such as requirements for increased situational awareness; (c) changes the roles of people in the system, often relegating people to supervisory controllers; (d) automation increases coupling and integration among parts of a system often resulting in data overload and "transparency"; and (e) the adverse impacts of automation is often not appreciated by those who advocate the technology. These changes can result in lower job satisfaction (automation seen as dehumanizing human roles), lowered vigilance, fault-intolerant systems, silent failures, an increase in cognitive workload, automation-induced failures, over-reliance, complacency, decreased trust, manual skill erosion, false alarms, and a decrease in mode awareness (Wiener, 1989).Adaptive AutomationDisadvantages of automation have resulted in increased interest in advanced automation concepts. One of these concepts is automation that is dynamic or adaptive in nature (Hancock & Chignell, 1987; Morrison, Gluckman, & Deaton, 1991; Rouse, 1977; 1988). In an aviation context, adaptive automation control of tasks can be passed back and forth between the pilot and automated systems in response to the changing task demands of modern aircraft. Consequently, this allows for the restructuring of the task environment based upon (a) what is automated, (b) when it should be automated, and (c) how it is automated (Rouse, 1988; Scerbo, 1996). Rouse(1988) described criteria for adaptive aiding systems:The level of aiding, as well as the ways in which human and aidinteract, should change as task demands vary. More specifically,the level of aiding should increase as task demands become suchthat human performance will unacceptably degrade withoutaiding. Further, the ways in which human and aid interact shouldbecome increasingly streamlined as task demands increase.Finally, it is quite likely that variations in level of aiding andmodes of interaction will have to be initiated by the aid rather thanby the human whose excess task demands have created a situationrequiring aiding. The term adaptive aiding is used to denote aidingconcepts that meet [these] requirements.Adaptive aiding attempts to optimize the allocation of tasks by creating a mechanism for determining when tasks need to be automated (Morrison, Cohen, & Gluckman, 1993). In adaptive automation, the level or mode of automation can be modified in real time. Further, unlike traditional forms of automation, both the system and the pilot share control over changes in the state of automation (Scerbo, 1994; 1996). Parasuraman, Bahri, Deaton, Morrison, and Barnes (1992) have argued that adaptive automation represents the optimal coupling of the level of pilot workload to the level of automation in the tasks. Thus, adaptive automation invokes automation only when task demands exceed the pilot's capabilities. Otherwise, the pilot retains manual control of the system functions. Although concerns have been raised about the dangers of adaptive automation (Billings & Woods, 1994; Wiener, 1989), it promises to regulate workload, bolster situational awareness, enhance vigilance, maintain manual skill levels, increase task involvement, and generally improve pilot performance.Strategies for Invoking AutomationPerhaps the most critical challenge facing system designers seeking to implement automation concerns how changes among modes or levels of automation will be accomplished (Parasuraman e t al., 1992; Scerbo, 1996). Traditional forms of automation usually start with some task or functional analysis and attempt to fit the operational tasks necessary to the abilities of the human or the system. The approach often takes the form of a functional allocation analysis (e.g., Fitt's List) in which an attempt is made to determine whether the human or the system is better suited to do each task. However, many in the field have pointed out the problem with trying to equate the two in automated systems, as each have special characteristics that impede simple classification taxonomies. Such ideas as these have led some to suggest other ways of determining human-automation mixes. Although certainly not exhaustive, some of these ideas are presented below.Dynamic Workload Assessment.One approach involves the dynamic assessment o fmeasures t hat index the operators' state of mental engagement. (Parasuraman e t al., 1992; Rouse,1988). The question, however, is what the "trigger" should be for the allocation of functions between the pilot and the automation system. Numerous researchers have suggested that adaptive systems respond to variations in operator workload (Hancock & Chignell, 1987; 1988; Hancock, Chignell & Lowenthal, 1985; Humphrey & Kramer, 1994; Reising, 1985; Riley, 1985; Rouse, 1977), and that measures o f workload be used to initiate changes in automation modes. Such measures include primary and secondary-task measures, subjective workload measures, a nd physiological measures. T he question, however, is what adaptive mechanism should be used to determine operator mental workload (Scerbo, 1996).Performance Measures. One criterion would be to monitor the performance of the operator (Hancock & Chignel, 1987). Some criteria for performance would be specified in the system parameters, and the degree to which the operator deviates from the criteria (i.e., errors), the system would invoke levels of adaptive automation. For example, Kaber, Prinzel, Clammann, & Wright (2002) used secondary task measures to invoke adaptive automation to help with information processing of air traffic controllers. As Scerbo (1996) noted, however,"...such an approach would be of limited utility because the system would be entirely reactive."Psychophysiological M easures.Another criterion would be the cognitive and attentional state of the operator as measured by psychophysiological measures (Byrne & Parasuraman, 1996). An example of such an approach is that by Pope, Bogart, and Bartolome (1996) and Prinzel, Freeman, Scerbo, Mikulka, and Pope (2000) who used a closed-loop system to dynamically regulate the level of "engagement" that the subject had with a tracking task. The system indexes engagement on the basis of EEG brainwave patterns.Human Performance Modeling.Another approach would be to model the performance of the operator. The approach would allow the system to develop a number of standards for operator performance that are derived from models of the operator. An example is Card, Moran, and Newell (1987) discussion of a "model human processor." They discussed aspects of the human processor that could be used to model various levels of human performance. Another example is Geddes (1985) and his colleagues (Rouse, Geddes, & Curry, 1987-1988) who provided a model to invoke automation based upon system information, the environment, and expected operator behaviors (Scerbo, 1996).Mission Analysis. A final strategy would be to monitor the activities of the mission or task (Morrison & Gluckman, 1994). Although this method of adaptive automation may be themost accessible at the current state of technology, Bahri et al. (1992) stated that such monitoring systems lack sophistication and are not well integrated and coupled to monitor operator workload or performance (Scerbo, 1996). An example of a mission analysis approach to adaptive automation is Barnes and Grossman (1985) who developed a system that uses critical events to allocate among automation modes. In this system, the detection of critical events, such as emergency situations or high workload periods, invoked automation.Adaptive Automation Human Factors IssuesA number of issues, however, have been raised by the use of adaptive automation, and many of these issues are the same as those raised almost 20 years ago by Curry and Wiener (1980). Therefore, these issues are applicable not only to advanced automation concepts, such as adaptive automation, but to traditional forms of automation already in place in complex systems (e.g., airplanes, trains, process control).Although certainly one can make the case that adaptive automation is "dressed up" automation and therefore has many of the same problems, it is also important to note that the trend towards such forms of automation does have unique issues that accompany it. As Billings & Woods (1994) stated, "[i]n high-risk, dynamic environments...technology-centered automation has tended to decrease human involvement in system tasks, and has thus impaired human situation awareness; both are unwanted consequences of today's system designs, but both are dangerous in high-risk systems. [At its present state of development,] adaptive ("self-adapting") automation represents a potentially serious threat ... to the authority that the human pilot must have to fulfill his or her responsibility for flight safety."The Need for Human Factors Research.Nevertheless, such concerns should not preclude us from researching the impact that such forms of advanced automation are sure to have on human performance. Consider Hancock’s (1996; 1997) examination of the "teleology for technology." He suggests that automation shall continue to impact our lives requiring humans to co-evolve with the technology; Hancock called this "techneology."What Peter Hancock attempts to communicate to the human factors community is that automation will continue to evolve whether or not human factors chooses to be part of it. As Wiener and Curry (1980) conclude: "The rapid pace of automation is outstripping one's ability to comprehend all the implications for crew performance. It is unrealistic to call for a halt to cockpit automation until the manifestations are completely understood. We do, however, call for those designing, analyzing, and installing automatic systems in the cockpit to do so carefully; to recognize the behavioral effects of automation; to avail themselves of present andfuture guidelines; and to be watchful for symptoms that might appear in training andoperational settings." The concerns they raised are as valid today as they were 23 years ago.However, this should not be taken to mean that we should capitulate. Instead, becauseobservation suggests that it may be impossible to fully research any new Wiener and Curry’ｓtechnology before implementation, we need to form a taxonomy and research plan tomaximize human factors input for concurrent engineering of adaptive automation.Classification of Human Factors Issues. Kantowitz and Campbell (1996)identified some of the key human factors issues to be considered in the design of advancedautomated systems. These include allocation of function, stimulus-response compatibility, andmental models. Scerbo (1996) further suggested the need for research on teams,communication, and training and practice in adaptive automated systems design. The impactof adaptive automation systems on monitoring behavior, situational awareness, skilldegradation, and social dynamics also needs to be investigated. Generally however, Billings(1997) stated that the problems of automation share one or more of the followingcharacteristics: Brittleness, opacity, literalism, clumsiness, monitoring requirement, and dataoverload. These characteristics should inform design guidelines for the development, analysis,and implementation of adaptive automation technologies. The characteristics are defined as: ?Brittleness refers to "...an attribute of a system that works well under normal or usual conditions but that does not have desired behavior at or close to some margin of its operating envelope."?Opacity reflects the degree of understanding of how and why automation functions as it does. The term is closely associated with "mode awareness" (Sarter & Woods, 1994), "transparency"; or "virtuality" (Schneiderman, 1992).?Literalism concern the "narrow-mindedness" of the automated system; that is, theflexibility of the system to respond to novel events.?Clumsiness was coined by Wiener (1989) to refer to automation that reduced workload demands when the demands are already low (e.g., transit flight phase), but increases them when attention and resources are needed elsewhere (e.g., descent phase of flight). An example is when the co-pilot needs to re-program the FMS, to change the plane's descent path, at a time when the co-pilot should be scanning for other planes.?Monitoring requirement refers to the behavioral and cognitive costs associated withincreased "supervisory control" (Sheridan, 1987; 1991).?Data overload points to the increase in information in modern automated contexts (Billings, 1997).These characteristics of automation have relevance for defining the scope of humanfactors issues likely to plague adaptive automation design if significant attention is notdirected toward ensuring human-centered design. The human factors research communityhas noted that these characteristics can lead to human factors issues of allocation of function(i.e., when and how should functions be allocated adaptively); stimulus-response compatibility and new error modes; how adaptive automation will affect mental models,situation models, and representational models; concerns about mode unawareness and-of-the-loop” performance problem; situation awareness decay; manual skill decay and the “outclumsy automation and task/workload management; and issues related to the design of automation. This last issue points to the significant concern in the human factors communityof how to design adaptive automation so that it reflects what has been called “team-centered”;that is, successful adaptive automation will l ikely embody the concept of the “electronic team member”. However, past research (e.g., Pilots Associate Program) has shown that designing automation to reflect such a role has significantly different requirements than those arising in traditional automation design. The field is currently focused on answering the questions,does that definition translate into“what is it that defines one as a team member?” and “howUnfortunately, the literature also shows that the designing automation to reflect that role?” answer is not transparent and, therefore, adaptive automation must first tackle its own uniqueand difficult problems before it may be considered a viable prescription to currenthuman-automation interaction problems. The next section describes the concept of the electronic team member and then discusses t he literature with regard to team dynamics, coordination, communication, shared mental models, and the implications of these foradaptive automation design.Adaptive Automation as Electronic Team MemberLayton, Smith, and McCoy (1994) stated that the design of automated systems should befrom a team-centered approach; the design should allow for the coordination betweenmachine agents and human practitioners. However, many researchers have noted that automated systems tend to fail as team players (Billings, 1991; Malin & Schreckenghost,1992; Malin et al., 1991;Sarter & Woods, 1994; Scerbo, 1994; 1996; Woods, 1996). Thereason is what Woods (1996) calls “apparent simplicity, real complexity.”Apparent Simplicity, Real Complexity.Woods (1996) stated that conventional wisdomabout automation makes technology change seem simple. Automation can be seen as simply changing the human agent for a machine agent. Automation further provides for more optionsand methods, frees up operator time to do other things, provides new computer graphics and interfaces, and reduces human error. However, the reality is that technology change has often。
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附录英文资料及其译文CHAPTER 1 Basis of the automatic control1. 1. Out lineThis device is temperature control experimental device of temperature, lever, pressure , that are the most common control subject for water treatment plants, chemical factories and power plants.At the experiment of automatic control, it is very important to keep the balance of system. Automatic control system is composed of control subjects and to keep control device. Especially process control has many control subjects to keep self-balance, so the experimental results is conducted with balance condition.This experimental device experiments about the relation of input signal and output signal to keep the balance (System characteristics) at the cascade experiment and fixed command control, also experiments on dynamic characteristic (Balance condition without time variation),static characteristic (Balance condition considerating time variation).And you record the change of control amount which was input handling amount or establishment of characteristic experiment. These typical response can be thought step response . Y ou experiment optimum control experiment , fixing P . I. D constant of control device, relating this step response.2. Process controlProcess control keeps the balance of the system, automatically correcting toward deviation, and compares every variable to operating toward deviation, and compares every variable to operation condition of industrial process of flux, temperature, surface, pressure, etc with the established goal. Process control is decided into closed loop and Open loop control.3. Kinds of automatic controlControl is composed of detection (measurement), comparison, judgment, operation and manual control is done by man’s hand and automatic control is done by measure or adjuster or other machinery. At present, automatic control is taken at the many plants .Moreover , we cannot think plant control without automatic control.Kinds of automatic control is decided like below.Automatic control Open loop controlSequential controlClosed loop control Feedback control3-1 .Feed back controlFeed back control detects (measure) compares , judges ,operates ,automatically. And it measures the results every moment , and corrects automatically if there is any differential between the desired value (set value).Feed back control is one of the main process control .This is the control that detecting (measuring) the results (Control amount: tank water lever ) with differential transmitter, and comparing the value with the set water lever and correct the deviation(Opening operation flow control of the control value)Like the above, feed back control automatically does the movement of measurement(detection) comparison calculation modernfication. Feed back system has the element of doing these functions.At the diagram 3-1 block line diagram it is the closed loop and it transmitted the correcting signal of the opposition toward the process flow.Diagram 3—1 Block line diagram of feed back control corollaryBlock line diagram: Indicating the constructing element of control system in block , and connecting the line expressing the signal flow.Signal :The amount being used for transmit the information3-2.Feed forward controlFeed forward control is the control method doing necessary correct movement before the influence appears to the control system , when disturbance enters into the control system.At feed back control, it starts correct movement to erase the deviation after the influence by disturbance appears, so in case of sudden change of disturbance or set value, then control disorders transiently and arises many problems.Feed forward control breaks down this weak point.At feed forward control, results of the controller is not done feedback and becomes open-loop control, not being close-loop control. Accordingly, in case of feed back control, the relation between cause and effect of disturbance and the results of control must be understood well. That is ,relation of influence to the control amount toward load change, and operating amount needed to compensate it should be clear.But it is generally difficult. In many cases it is difficult that all the disturbance is detected, andto get the perfect process model in actual process.Moreover it is difficult to avoid constant deviation, so it is used combined with feed back control.3-3 Sequence controlSequence control processes the fixed process in advance step by step. This sequence control is used in neon signs, vending machines, or electric washing machines. On the other hand, it is needed in the producing process at chemical factory, automobile factory, conveyer, automatic warehouse as automation.3.4 Classification of control methodsProduction method by continuous process is employed, when scale of process industry gets bigger and larger production amount id requested.You need to make proper use of the control method on some different condition of operation. To classfy automatic control system is difficult strictly because their control system has correlation. But general method of cassification will be like below.4-1 Classification by desired valueAutomatic control Fixed command controlFollowing control Follow up controlProgram control4-1-1 Fixed command controlMost of the controls belong to this control when desired value is constant.4-1-2 Following command controlServo mechanism composed to follow the change belongs to this control when desired value changes optionally.4-1-3 Program controlIt is used for the control in which desired value changes according to fixed time schedule. Normally , it is well used to temp. Control of fever process of metal or batch process at the chemical industry.4-2. Classification by control methodAutomatic control Unity control loopRatio controlCascade control4-2-1 ratio controlIt is well used to flow control of 2 fluid when desired value keeps with other fluid (other amount) constantly.You compare and measure if special ratio keeps toward basic fluid (B), then maintain the ratio as the purpose.4-2-2 Cascade controlIt is the control method that more than 2 automatic control systems control by output of other control device. In short, control circuit is combined in series.You change the desired value of flow by output of flux adjuster. If you control the desired value of flow control to fix water level in water tank, then you can prevent the influence to the level such as main pressure change of flow itself.The purpose of cascade control method is to absorb disturbance at first control, also to make easy the next step control, and to progress the control on the whole.3.5 Control actionAt control action, movement to reduce control deviation, giving operating amount in accordance with a movement signal is called control action.The following is this classification.5-1 Continuous controlIt does constantly correct movement toward deviation with desired value,measuring continuously toward controlled variable change.5-1-1 Proportional actionIt is the movement that operating amount Y is in proportion to movement signal and deviation. Suppose you make deviation as e, gain as kp,3.6 Automatic control apparatusFollowing apparatus are necessary for each part, like a block diagram( See diagram 3-2) of feedback control.◇ Primary Detecting element◇ Final control element◇ Controlling means6-1 Primary Detecting elementThis is the part to take out necessary signals from controlling object , and transmit on control devece.(1)Expanding thermometer (Bimetal type)Bimetal is the joined two metal boards which have different thermal expansion coefficient. It curves by temp.change.(2)Pressure type thermometer ( Vapor pressure )You put vaporlizableliquied like propane or ether into thermal sensitivity part.These liquid has certain vapor pressure, by contact surface with gas, and it deflects the needle, by changing pressure measuring mechanism.(3)Resistance thermometerIt is well known that in thermometry resistor (Rt). Its resistance increases as electric resistance changes by temp. and temp. rises.You build a bridge with thermometry resistor, and when its resistance value changes by temp., unbalanced current generates at the bridge. From this, you will know the temperature.(4) Thermoelectric thermometer (Thermocouple)Zeebesk effect is employed. You connect different kinds of metal. And make one connecting point temp.(Thermometry contact point) higher than another connecting point (Standard contact point) . Then thermoelectromotive force is generated between both connecting points.(5) Radiant thermometerSolid emits radiant energy in proportion to the square of four of absolute temp., like Stefan-Boltzmann’s formula.(6) Thermocouple and thermometry resistorDetecting part (Temp.sensor) of temp.control experimental device (SPC-201) is mostly used in industrial tem.measurement.And it employs thermocouple and thermometry resistor with which you can measure in high accuracy and easily.6-2. Final control element ( Control valve)Control valve receives output of adjuster and converts it into process variable Control valve isthe element to feedback to the process. So decides valve specifications. Understanding each controlled objects.6-2-1 Control valve feature(1) Flow feature( Proper feature)Relation of lift of control valve. That is , valve operation signal, with flux is called flow feature,equal percentage feature and on-off feature represent.1、near feature2、queral percentage feature3、On-off feature①Linear featureValve opening degree and flow are in proportion. Accordingly, flux changes 10% linearly,as opening degree changes 10%.②Equal percentage featureFlow increasing modulus becomes constant toward unit change of valve lift . Suppose there is flow increasing modulus at 10%, toward n% change of lift. Then flow will be.When 10 L/min weighting is 10*0.1=1 L/minWhen 50 L/min weighting is 50*0.1=1 L/minWhen 90 L/min weighting is 90*0.1=1 L/minEven if the same n% lift change, flow change is small, if flow is small. And it becomes big if flow is big. So, it has fine adjustment feature over wide flow range.③ On-off featureFlow soon reaches to the max.value, when a valve shift starts to move. It is usually used for 2-positioning control.It is called on-off feature or quick open feature.7.Relation of PID value and disturbance and control response toward setting change.Collect the relationship of PID value and control response toward disturbance, from experimental results, acquired at experiments.(1)proportional (P) action effect••••••••••••To increase proportional gain•To decrease off set amount (In case of no-integral action)•To become oscillative ,and be hunting condition•To get shouter the period of oscillation(2)Integral (I) action resultsFollowing things are conducted in aexperiment ,changed only integral time T I and shortening T I.•To lose off set amount•The first mountain gets smaller•To become oscillative. Amplitude damping ratio gets bigger to finally exhale•Returning time initially to setting value is shortened.(3)Differential (D) action effectFollowing thing is conducted in aexperiment ,changing only differential time and making T D longer.•Off set amount does not change (In case of no-integral action)•The first mountain gets smaller•Oscillation i s restrained. Amplitude damping ratio gets smaller. But ,if differential time is too long ,it becomes oscillative again.•Oscillaion period is shortened.8. P.I.D adjustment procedureYou shall understand PID feature and nature from experiments you did ,however, have difficulty in finding true value ,despite the adjustment .(1)Adjust P-I-D in order. Make step change toward desirable value, changing each constant.Then confirm and record the results.(2)Change proportional (P) band from large number to small number. Stop it when measuringvalue causes hunting.(3)Change integral time (T I) from large number to small number, too. Stop it when measuringvalue causes hunting, and bring a little back larger.(4)Change differential time (T D) from small number to large number. Stop it when measuringvalue causes hunting, and bring a little back smaller.(5)When you wish to shorten setting time, make P much smaller, and check the change ofmeasuring value.9. Optimum adjustmentTo arrange the control purpose based on the former experiments.(1)When you change desirable value, make measuring value and desirable value agree, as soonas possible. (Desirable value change, step response)(2)If measuring value deviates from desirable value, owing to changes of surrounded conditiontemporarily, put it back as soon as possible. (Disturbance response)As for (1) , time passage to make them agree is the problem. Measuring value change on PID value, in case of desirable value change, will be normally like diagram 9-1.At this diagram, temporarily it gets over desirable value. This is called overshoot. To decrease this over amount, and to shorted time to settle down measuring value (Setting time) do not normally coexist. In short, adjusting not to get over makes the setting time longer.Also, control process may have some trouble, if you shorten the setting time, ignoring over amount. On the other hand, it may be better to reach the desirable value sooner in spite of over amount. So, establishing guideline of overshoot, and you have and adjusting method, making time minimum within the range. It is called optimum adjustment, to adjust PID constant.自动控制基础1、提纲这套设备是用来测量温度、流量、液位、压力的实验设备中的温度的设备。