Tuning of fuzzy PID controller for Smith predictor
PID Control System Analysis and Design
➢ Increases the phase-lag
➢ Gain margin (GM) and phase margin (PM) are reduced, and the closed-loop system becomes more oscillatory and potentially unstable
Integrator Windup Remedies
➢ Antiwindup can be achieved implicitly through automatic reset.
➢ Explicit Antiwindup implemented explicitly through internal negative feedback.
STANDARD STRUCTURES OF PID CONTROLLERS
➢ A PID controller is a phase lead-lag compensator with one pole at the origin and the other at infinity.
➢ PI-Phase lag.
➢ Unstable system
Remedies
➢ Involves use of filters
Linear low pass filter Velocity Feedback SetPoint Filter Nonlinear median filter
Linear low pass filter
➢ Windup is due to the controller states becoming inconsistent with the saturated control signal, and future correction is ignored until the actuator desaturates
基于FPGA的交叉耦合控制器的设计与实现
2011年第3期现代制造工程(Modern Manufacturing Engineering)设备设计/诊断维修/再制造基于FPGA的交叉耦合控制器的设计与实现*张团善，潘铜，叶小荣，张娜(西安工程大学电子信息学院，西安710048)摘要:针对运动控制系统多轴联动的协同问题，分析轮廓误差产生机理和交叉耦合控制算法。
利用现场可编程门阵列(Field-Programmable Gate Array，FPGA)内部丰富的查找表资源，结合模糊自整定PID控制算法的特点，通过MATLAB软件的Fuzzy工具箱完成模糊逻辑策略的建立，利用离线计算、在线查表的方法设计出一种基于FPGA的交叉耦合控制器。
最终通过Quartus II软件完成整个系统的分析、综合和功能仿真，并利用Link for Modelsim软件实现对硬件设计电路的验证。
仿真结果表明该方法可以有效地减少轮廓误差，提高运动控制系统的调节和跟踪性能。
关键词:交叉耦合;轮廓误差;可编程门阵列;模糊自整定PID中图分类号:TM57文献标志码:A文章编号:1671—3133(2011)03—0108—06Design and implementation of cross-coupledcontroller based on FPGAZHANG Tuan-shan，PAN Tong，YE Xiao-rong，ZHANG Na(Electronic Information College，Xi’an Polytechnic University，Xi’an710048，China) Abstract:In order to solve the collaborative problem of multi-axis motion control system，the contour error and cross-coupling control algorithm are analysed．Using the rich resource of lookup table in FPGA and the characteristics of fuzzy self-tuning PID control algorithm，FPGA-based cross-coupling controller is designed by the method of off-line computing，online look-up table，which uses the Fuzzy toolbox of MATLAB to complete the establishment of fuzzy logical strategies．Finally，the analysis，synthesis and functional simulation of the whole system are completed by Quartus II，and the verification of hardware circuit design is real-ized by Link for Modelsim．Simulation results show that this method can effectively improve the motion control system’s regulating and tracking performance．Key words:cross coupled control;contour error;FPGA;fuzzy self-tuning PID0引言运动控制系统通常有多个轴需要伺服，多轴联动必然引起较大的轮廓误差，如何减少轮廓误差提高轮廓轨迹的控制精度，是实现多轴同步控制的关键问题。
基于直流伺服系统的模糊自适应控制应用
ｔｈｅ Ｄ Ｃ ｓｅｒｖｏ ｓｙｓｔｅｍ
Ｔａｉ Ｊｉｎｇ，Ｗ ａｎｇ Ｚｈｏｎｇｑｉｎｇ （Ｎｏｒｔｈ Ｕｎｉｖｅｒｓｉｔｙ ｏｆＣｈｉｎａ，Ｔａｉｙｕａｎ０３００５１，Ｓｈａｎｘｉ，Ｃｈｉｎａ）
Ａｂｓｔｒａｃｔ：Ｉｎ ｔｈｅ ｅｓｔａｂｌｉｓｈｍ ｅｎｔ ｏｆ ａ ｍａｔｈｅｍ ａｔｉｃａｌ ｍ ｏｄｅｌ ｏｆ ｂｒｕｓｈｌｅｓｓ ＤＣ ｂａｓｅｄ ｏｎ ｔｈｅ ＤＣ ｍｏｔｏｒ ｓｅｒｖｏ ｓｙｓｔｅｍ ｆｏｒ ｔｈｅ ｅｘｉｓｔｉｎｇ ｎｏｎ—ｌｉｎｅａｒ，ｓｔｒｏｎｇ ｃｏｕｐｌｉｎｇ ａｎｄ ｓｔｒｕｃｔｕｒａｌ ｃｈａｒａｃｔｅｒｉｓｔｉｃｓ ｏｆ ａ ｌａｒｇｅ ｒａｎｇｅ ｏｆ ｐａｒａｍ ｅｔｅｒｓ ｏｆ ｆｕｚｚｙ ｒｅａｓｏｎｉｎｇ， ｐａｉｒｓ ｏｆ ｃｏｎｔｒｏｌ ｐａｒａｍｅｔｅｒｓ（Ｋｐ，Ｋｉ，Ｋｄ）ｏｎ—ｌｉｎｅ ｔｕｎｉｎｇ，ｉｎ ｏｒｄｅｒ ｔｏ ａｃｈｉｅｖｅ ｏｐｔｉｍａｌ ｃｏｎｔｒｏ１．Ｔｈｒｏｕｇｈ ｔｈｅ Ｍ ＡＴＬＡＢ ／Ｓｉｍ ｕｌｉｎｋ ｓｉｍｕｌａｔｉｏｎ ｔｏ ｂｅ ｃｏｎｓｉｓｔｅｎｔ ｗｉｔｈ ｔｈｅ ｔｈｅｏｒｅｔｉｃａｌ ａｎａｌｙｓｉｓ ｏｆ ｔｅｓｔ ｒｅｓｕｌｔｓ，ｗｈｉｌｅ ｆｕｚｚｙ ａｄａｐｔｉｖｅ ＰＩＤ ｃｏｎｔｒｏｌ ｉｎ ｂｒｕｓｈｌｅｓｓ Ｄ Ｃ ｍ ｏｔｏｒ ｓｙｓｔｅｍ ｉｎ ｔｈｅ ａｐｐｌｉｃａｔｉｏｎ ｏｆ ａ ｓｉｍ ｕｌａｔｉｏｎ ｓｔｕｄｙ，ｓｉｍ ｕｌａｔｉｏｎ ｒｅｓｕｌｔｓ ｖａｌｉｄａｔｅ ｔｈｅ ｔｒａｄｉｔｉｏｎａｌ ＰＩＤ
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对 ＢＬＤＣ控制 系统进行数字仿 真 ，建立 可以大大加
三菱PLCPID调节手册
Programming ManualMitsubishi Programmable Logic ControllerQCPU(Q Mode)/QnACPU(PID Control Instructions)• SAFETY CAUTIONS •(You must read these cautions before using the product)In connection with the use of this product, in addition to carefully reading both this manual and the related manuals indicated in this manual, it is also essential to pay due attention to safety and handle the product correctly.The safety cautions given here apply to this product in isolation. For information on the safety of the PC system as a whole, refer to the CPU module User's Manual.Store this manual carefully in a place where it is accessible for reference whenever necessary, and forward a copy of the manual to the end user.REVISIONS* The manual number is given on the bottom left of the back cover.Print Date* Manual Number RevisionDec., 1999SH (NA) 080040-A First editionJun., 2001SH (NA) 080040-B Partial additionAbout Manuals, Chapter 1, Chapter 2, Section 2.1, 3.1, 3.2, 3.3, 3.3.1,4.2.3, 4.3.2, 4.3.5, Chapter 5, Section5.1, 5.2, Chapter 6, Chapter 7,Section 8.1, 8.2Japanese Manual Version SH-080022-BThis manual confers no industrial property rights or any rights of any other kind, nor does it confer any patent licenses. Mitsubishi Electric Corporation cannot be held responsible for any problems involving industrial property rights whichmay occur as a result of using the contents noted in this manual.1999 MITSUBISHI ELECTRIC CORPORATIONINTRODUCTIONThank you for choosing the Mitsubishi MELSEC-Q/QnA Series of General Purpose Programmable Controllers. Please read this manual carefully so that the equipment is used to its optimum. A copy of this manual should be forwarded to the end User.CONTENTS1. GENERAL DESCRIPTION 1 – 1 to 1 - 21.1 PID Processing Method...........................................................................................................................1 - 22. SYSTEM CONFIGURATION FOR PID CONTROL 2 - 1 to 2 - 22.1 Applicable PLC CPU................................................................................................................................2 - 13. PID CONTROL SPECIFICATIONS 3 - 1 to 3 - 63.1 Performance Specifications.....................................................................................................................3 - 1 3.2 Operation Expressions.............................................................................................................................3 - 1 3.3 PID Control Instruction List......................................................................................................................3 - 23.3.1 How to read the instruction list..........................................................................................................3 - 33.3.2 PID operation instruction list.............................................................................................................3 - 54. PID CONTROL 4 - 1 to 4 - 124.1 Outline of PID Control..............................................................................................................................4 - 1 4.2 PID Control...............................................................................................................................................4 - 24.2.1 Operation method..............................................................................................................................4 - 24.2.2 Normal operation and reverse operation..........................................................................................4 - 24.2.3 Proportionate operation (P operation)..............................................................................................4 - 44.2.4 Integrating operation (I operation)....................................................................................................4 - 54.2.5 Differentiating operation (D operation).............................................................................................4 - 64.2.6 PID operation.....................................................................................................................................4 - 7 4.3 PID Control Functions..............................................................................................................................4 - 74.3.1 Bumpless changeover function.........................................................................................................4 - 74.3.2 MV higher/lower limit control function...............................................................................................4 - 84.3.3 Monitorning PID control with the AD57(S1) (QnACPU only)...........................................................4 - 94.3.4 Function for transfer to the SV storage device for the PV in manual mode..................................4 - 104.3.5 Changing PID Control Data or input/output Data Setting Range(High Performance model QCPU Only).........................................................................................4 - 11 5. PID CONTROL PROCEDURE 5 - 1 to 5 - 105.1 PID Control Data......................................................................................................................................5 - 35.1.1 Number of loops to be used and the number of loops to be executed in a single scan.................5 - 65.1.2 Sampling cycle..................................................................................................................................5 - 7 5.2 Input/Output Data.....................................................................................................................................5 - 86. PID CONTROL INSTRUCTIONS 6 - 1 to 6 - 27. HOW TO READ EXPLANATIONS FOR INSTRUCTIONS7 - 1 to 7 - 28. PID CONTROL INSTRUCTIONS8 - 1 to 8 - 108.1 PID Control Data Settings.........................................PIDINIT,PIDINITP................................................8 - 2 8.2 PID Control ...............................................................PIDCONT,PIDCONTP.........................................8 - 3 8.3 Monitoring PID Control Status (QnACPU only).......PID57,PID57P......................................................8 - 5 8.4 Operation Stop/Start of Designated Loop No..........PIDSTOP,PIDSTOPP,PIDRUN,PIDRUNP.........8 - 8 8.5 Parameter Change at Designated Loop...................PIDPRMW,PIDPRMWP......................................8 - 99. PID CONTROL PROGRAM EXAMPLES9 - 1 to 9 - 109.1 System Configuration for Program Examples.........................................................................................9 - 1 9.2 Program Example for Automatic Mode PID Control...............................................................................9 - 2 9.3 Program Example for Changing the PID Control Mode between Automatic and Manual....................9 - 6APPENDIX APP - 1APPENDIX 1 PROCESSING TIME LIST................................................................................................APP – 1About ManualsThe following manuals are also related to this product.In necessary, order them by quoting the details in the tables below. Related ManualsManual Name Manual Number (Model Code)High Performance model QCPU (Q mode) User's Manual(Function Explanation/Program Fundamentals)Describes the functions, programming procedures, devices, parameter types and program types necessary in program creation using QCPU (Q mode).(Option)SH-080038 (13JL98)QnACPU Programming Manual (Fundamentals)Describes how to create programs, the names of devices, parameters, and types of program.(Option)IB-66614 (13JF46)QCPU (Q mode) /QnACPU Programming Manual (Common Instructions)Describes how to use sequence instructions, basic instructions, and application instructions.(Option)SH-080039 (13JF58)QnACPU Programming Manual (Special Function)Describes the dedicated instructions for special function modules available when using theQ2ACPU(S1), Q3ACPU, and Q4ACPU.(Option)SH-4013 (13JF56)QnACPU Programming Manual (AD57 Instructions)Describes the dedicated instructions for controlling an AD57(S1) type CRT controller module available when using the Q2ACPU(S1), Q3ACPU, or Q4ACPU.(Option)IB-66617 (13JF49)QCPU (Q mode) / QnACPU Programming Manual (SFC)Describes the system components, performance specifications, and functions, protramming, debugging and error codes of MELSAP-3(Option)SH-080041 (13JF60)Q4ARCPU Programming Manual (Application PID Edition)Describes the programming procedures and device name necessary in program creation to control Applied PID using process control instructions.(Option)IB-66695 (13JF52)Before reading this manual, refer to High Performance model QCPU (Q mode) User'sManual (Function Explanation/Programming Fundamentals) and QnACPUProgramming Manual (Fundamentals) in order to confirm the programs, I/Oprocessing, and devices used with High Performance model QCPU(Q mode)/QnACPU.Describes the instructionsused for Applied PIDcontrol.Generic Names:High Performance model QCPU...Generic names for Q02CPU, Q02HCPU, Q06HCPU, Q12HCPU, Q25HCPU QnACPU ........................................Generic names for Q2ASCPU, Q2ASCPU-S1, Q2ASHCPU, Q2ASHCPU-S1, Q2ACPU, Q3ACPU, Q4ACPU, Q4ARCPUCPU module....................................Generic names for QnACPU, High Performance model QCPU1. GENERAL DESCRIPTION1 This manual describes the sequence program instructions used to execute PID controlwith the High Performance model QCPU/QnACPU.The High Performance model QCPU /QnACPU has the capability to use instructionsfor PID control as a standard feature, so PID control can be executed by loading anA/D conversion module and a D/A conversion module.In addition, the PID control status can be monitored with an AD57(S1).POINTThe Basic model QCPUs (Q00JCPU, Q00CPU, Q01CPU) are not compatible withthe PID control instructions.Use the High Performance model QCPU to use the PID control instructions.REMARKThe High Performance model QCPU is the generic term of the Q02CPU, Q02HCPU,Q06HCPU, Q12HCPU and Q25HCPU.Any of them is abbreviated to the High Performance model QCPU in this manual.1.1 PID Processing MethodThis section describes the processing method for PID control using PID controlinstructions. (For details on PID operations, see Chapter 4.)Execute PID control with PID control instructions by loading an A/D conversion moduleand a D/A conversion module, as shown in Figure 1.1.As shown in Figure 1.1, using the previously set SV (set value) and the digital PV(process value), which is read from the A/D conversion module, PID operation isexecuted to obtain the MV (manipulated value).The calculated MV (manipulated value) is output to the D/A conversion module.The sampling cycle is measured, and the PID operation is performed, when thePIDCONT instruction is executed in the sequence program, as illustrated below.PID operation in accordance with the PIDCONT instruction is executed in presetsampling cycles.MELSEC-Q/QnA2. SYSTEM CONFIGURATION FOR PID CONTROL22. SYSTEM CONFIGURATION FOR PID CONTROLThis section describes the system configuration for PID control using PID control instructions.(For details on the units and modules that can be used when configuring the system, refer to the manual for the CPU module used.)CRTOperation panelD/A conversion moduleA/D conversion moduleMain base unitExtension cableExtension base unitPV (process value) inputFor MV (manipulated value) outputFor PID control monitoring (Only QnACPU)CRT control module AD57 or AD57-S1 onlyQnACPUQCPU High Performance modelPOINT(1) For QnACPU, the reference range for SV, PV, and MV values used in PID operations is 0 to 2000. If the resolution of the A/D conversion module or D/Aconversion module used for input/output in PID control is not 0 to 2000, convert the digital values to 0 to 2000.(2) For High Performance model QCPU, a setting is selectable from fixed values as described in (1) or any appropriate values for the unit used. See Section 4.3.5for details.2.1 Applicable PLC CPUComponent ModuleHigh Performance model QCPU Q02CPU, Q02HCPU, Q06HCPU, Q12HCPU, Q25HCPUQnACPUQ2ASCPU, Q2ASCPU-S1, Q2ASHCPU, Q2ASHCPU-S1Q2ACPU, Q3ACPU, Q4ACPU, Q4ARCPU2. SYSTEM CONFIGURATION FOR PID CONTROLMELSEC-Q/QnA MEMO33. PID CONTROL SPECIFICATIONSThis section gives the specifications PID control using PID control instructions.3.1 Performance SpecificationsThe performance specifications for PID control are tabled below.SpecificationQnACPUItemWith PID Limits for HighPerformance modelQCPU Without PID Limits forHigh Performance modelQCPUNumber of PID control loops—32 loops (maximum)Sampling cycle T S 0.01 to 60.00 sPID operation method—Process value differentiation (normal operation/reverse operation)Proportionate constant K P 0.01 to 100.00Integration constant T I 0.1 to 3000.0 s PID constant setting rangeDifferential constantT D 0.00 to 300.00 sSV (set value) setting range SV 0 to +2000-32768 to +32767PV (process value) setting range PV MV (manipulated value) output range MV-50 to +2050-32768 to +327673.2 Operation ExpressionsThe operation expressions for PID control using PID control instructions are indicated below.NameOperation ExpressionsMeanings of SymbolsNormal operationEV n =PV nf *-SVMV n = MV MV=K p {(EV n -EV n-1)+ EV n - (2PV nf-1-PV nf -PV nf-2)}T S T I T DT SProcess valuedifferentiationReverse operationEV n =SV-PV nf *MV n = MVMV=K p {(EV n -EV n-1)+ EV n + (2PV nf-1-PV nf-PV nf-2)}T ST I T D T S EV n : Deviation in the present sampling cycle EV n-1: Deviation in the preceding sampling cycleSV : Set valuePV nf : Process value of the present sampling cycle (after filtering)PV nf-1: Process value of the preceding samplingcycle (after filtering)PV nf-2: Process value of the sampling cycle two cycles before (after filtering)MV : Output change amount MV n : Present manipulation amount K P : Proportionate constant T S: Sampling cycle T I : Integration constantT D: Differential constant POINT(1) *:PV nf is calculated using the following expression.Therefore, it is the same as the PV (process value) of the input data as long as the filter coefficient is not set for the input data.Process Value after Filtering PV nf = PV n + (PV nf -1-PV n )PV n : Process value of the present sampling: Filter coefficientPV nf-1: Process value of the preceding sampling cycle (after filtering)(2) PV nf is stored in the I/O data area. (See Section 5.2)3.3 PID Control Instruction ListA list of the instructions used to execute PID control is given below.CPU Instruction Name Processing DetailsQ QnAPIDINIT Sets the reference data for PID operation.*1PIDCONT Executes PID operation with the SV (set value) and the PV (process value).*1PID57Used to monitor the results of PID operation at an AD57(S1).×PIDSTOP PIDRUN Stops or starts PID operation for the set loop No.PIDPRMWChanges the operation parameters for the designated loop number to PID control data.*1: For High Performance model QCPU, PID limits can be set to ON or OFF. SeeSections 5.1 and 5.2 for the setting range used in each mode.3.3.1 How to read the instruction listThe instruction list in Section 3.3.2 has the format indicated below:Table 3.1 How to Read the Instruction ListExplanation(1) Classification of instructions according to their application.(2) Instruction names written in a sequence program.(3) Symbols used in the ladder diagram.(4) Processing for each instruction.(5) The execution condition for each instruction. Details are given below.(6) Number of instruction stepsFor details on the number of steps, refer to the QCPU (Q mode) /QnACPU Programming Manual (Common Instructions).(7) A circle indicates that subset processing is possible.For details on subset processing, refer to the QCPU (Q mode) /QnACPU Programming Manual (Common Instructions).(8) Indicates the page number in this manual where a detailed description for theinstruction can be found.3.3.2 PID operation instruction list4.2 PID ControlThe operation methods for PID control with the PID control instructions are the speedmethod and process value differentiation method. The following describes the controlexecuted for both of these methods:4.2.1 Operation method(1) Speed method operationThe speed method operation calculates amounts of changes in the MVs(manipulated values) during PID operation.The actual MV is the accumulatedamount of change of the MV calculated for each sampling cycle.(2) Process value differentiation method operationThe process value differentiation method operation executes PID operations bydifferentiating the PV (process value).Because the deviation is not subject to differentiation, sudden changes in theoutput due to differentiation of the changes in the deviation generated bychanging the set value can be reduced.Either forward operation or reverse operation can be selected to designate thedirection of PID control.4.2.2 Normal operation and reverse operation(1) In normal operation, the MV (manipulated value) increases as the PV (processvalue) increases beyond the SV (set value).(2) In reverse operation, the MV (manipulated value) increases as the PV (processvalue) decreases below the SV (set value).(3) In normal operation and reverse operation, the MV (manipulated value) becomeslarger as the difference between the SV (set value) and the PV (process value)increases.(4) The figure below shows the relationships among normal operation and reverseoperation and the MV (manipulated value), the PV (process value), and the SV(set value):(5) The figure below shows examples of process control with normal operation andreverse operation:4.2.3 Proportionate operation (P operation)The control method for proportionate operation is described below.(1) In proportionate operation, an MV (manipulated value) proportional to thedeviation (the difference between the set value and process value) is obtained.(2) The relationship between E (deviation) and the MV (manipulated value) isexpressed by the following formula:MV=Kp • EKp is a proportional constant and is called the "proportional gain".(3) The proportionate operation in step response with a constant E (deviation) isillustrated in Fig. 4.2.(4) The MV (manipulated value) changes within the range from -50 to 2050 or theuser-defined range (for High Performance model QCPU only).The MV (manipulated value) in response to the same deviation becomes largeras Kp becomes larger, thus the compensating motion is greater.(5) The proportionate operation is always associated with an offset (offset error).4.2.4 Integrating operation (I operation)The control method for integrating operation is described below.(1) In the integrating operation, the MV (manipulated value) changes continuously tozero deviation when it occurs.This operation can eliminate the offset that is unavoidable in proportionateoperation.(2) The time required for the MV in integrating operation to reach the MV forproportionate operation after the generation of deviation is called the integratingtime. Integrating time is expressed as T I.The smaller the setting for T I, the more effective the integrating operation will be.(3) The integrating operation in step response with a constant E (deviation) isillustrated in Fig. 4.3.(4) Integrating operation is always used in combination with proportionate operation(PI operation) or with proportionate and differentiating operations (PID operation).Integrating operation cannot be used independently.4.2.5 Differentiating operation (D operation)The control method for differentiating operation is described below.(1) In differentiating operation, an MV (manipulated value) proportional to thedeviation change rate is added to the system value to zero deviation when itoccurs.This operation prevents significant fluctuation at the control objective due toexternal disturbances.(2) The time required for the MV in the differentiating operation to reach the MV forthe proportionate operation after the generation of deviation is called thedifferentiating time. Differentiating time is expressed as T D.The smaller the setting for T D, the more effective the differentiating operation willbe.(3) The differentiating operation in step response with a constant E (deviation) isillustrated in Fig. 4.4.(4) Differentiating operation is always used in combination with proportionateoperation (PD operation) or with proportionate and integrating operations (PIDoperation).Differentiating operation cannot be used independently.4.2.6 PID operationThe control method when proportionate operation (P operation), integrating operation (Ioperation), and differentiating operation (D operation) are used in combination isdescribed below.(1) During PID operation, the system is controlled by the MV (manipulated value)calculated in the (P + I + D) operation.(2) PID operation in step response with a constant E (deviation) is illustrated in Fig.4.5.4.3 PID Control FunctionsDuring PID control using the PID control instructions, MV upper/lower limit control isautomatically executed by the bumpless changeover function explained below.4.3.1 Bumpless changeover functionThis function controls the MV (manipulated value) continuously when the control modeis changed between manual and automatic.When the control mode is changed between manual and automatic, data is transmittedbetween the MV area for automatic mode and the MV area for manual mode.The control mode is changed in the input/output data area (see Section 5.2).(1) Changing from the manual ...........mode to the automatic mode The MV in the manual mode is transmitted to the MV area for the automatic mode.(2) Changing from the automatic .......mode to the manual mode The MV in the automatic mode is transmitted to the MV area for the manual mode.POINT(1) Manual and automatic modes of PID control:1) Automatic modePID operation is executed with a PID control instruction.The control object is controlled according to the calculated MV.2) Manual modePID operation is not executed. The MV is calculated by the user and thecontrol object is controlled according to the user-calculated MV.(2) The loop set in the manual mode stores the PV (process value) in the set valuearea every sampling cycle.4.3.2 MV higher/lower limit control functionThe MV higher/lower limit control function controls the higher or lower limit of the MVcalculated in the PID operation. This function is only effective in the automatic mode. Itcannot be executed in the manual mode.By setting the MV higher limit (MVHL) and the MV lower limit (MVLL), the MVcalculated in the PID operation can be controlled within the range between the limits.When the MV higher/lower limit control function is used, the MV is controlled asillustrated above.A MVHL (manipulated value higher limit) and MVLL (manipulated value lower limit)takes on a value between -50 and 2050 or a user-defined value (for High Performancemodel QCPU only).The following are the default settings:• Higher limit................2000 (Or user-defined value)• Lower limit................0 (Or user-defined value)The value set for the higher limit must not be smaller than the value set for the lowerlimit.An error will occur if it is.4.3.3 Monitoring PID control with the AD57(S1) (QnACPU only)The PID control operation results can be monitored in a bar graph with an AD57(S1)CRT controller unit.(1) The monitor screen displays the monitored information of eight loops beginningwith the designated loop number.POINTThe SV, PV, and MV present value are displayed as percentages of 2000.1) SV percentage display...............SV2000100 (%)2) PV percentage display...............PV2000100 (%)3) MV percentage display...............MV2000100 (%)(2) Use the PID57 instruction to execute monitoring with an AD57(S1).See Section 8.3 for details on the PID57 instruction.4.3.4 Function for transfer to the SV storage device for the PV in manual modeThe PIDCONT instruction is also executed in manual mode.In the manual mode, it ispossible to select whether or not the PV input from the A/D conversion module onexecution of the PIDCONT instruction is transferred to the SV storage device or not inaccordance with the ON/OFF status of the PID bumpless processing flag (SM774).• When SM774 is OFF : When the PIDCONT instruction is executed, the PV istransferred to the SV storage device.On switching from the manual mode to the automaticmode, the MV output is continued from the value in themanual mode.After switching to the automatic mode, control can beswitched from the MV that was being output to the SV bychanging the SV.• When SM774 is ON : When the PIDCONT instruction is executed, the PV is nottransferred to the SV storage device.On switching from the manual mode to the automaticmode, control can be switched from the MV output in themanual mode to the SV.Before switching to the automatic mode, store a SV in theSV storage device.POINTWhen SM774 is ON or OFF, switching from the manual mode to the automaticmode may cause different control effects as follows.• When SM774 is OFF, the PV is transferred to the SV storage device.When the manual mode is switched to the automatic mode, no difference iscaused between the PV and the SV and the MV does not change rapidly, exceptthat the SV differs from a target value defined in the automatic mode.Use the sequence program to make step-by-step adjustments to the SV so thatthe SV approaches closer to the target value.See sample programs in Section 9.3.• When SM774 is ON, the PV is not transferred to the SV storage device. This maycause a difference between the PV and the SV when the manual mode isswitched to the automatic mode.A greater difference may cause the MV to change rapidly. So this procedure isused for systems in which the manual mode can be switched to the automaticmode only when the PV approaches closer tothe SV.The automatic mode can be effected without using the sequence program tomake step-by-step adjustments to the SV.REMARKThe SV and PV are stored in the devices in the I/O data area designated by thePIDCONT instruction.4.3.5 Changing the PID Control Data or Input/Output Data Setting Range (HighPerformance model QCPU Only)For High Performance model QCPU, setting ranges can be selectable for PID controldata (see Section 5.1) and input/output data (see Section 5.2). To effect the user-defined setting range, designate the loops for which PID limit settings (SD774 and SD775) are defined, and then set these loops' bits to ON before executing the PIDCONTand PIDINT instructions.SD774SD7750 : PID Limit ON (default setting)1 : PID Limit OFF (user-defined setting)A "PID Limit OFF" setting does not effect the limit control over internal data. To effectthe limit control, execute the processing by operating from the user's application side.。
PID课设
课程设计题目：PID算法的MATLAB仿真比较分析系别：班级:姓名：学号：PID算法的MAT L AB仿真比较分析摘要PID控制器具有结构简单、容易实现、控制效果好、鲁棒性强等特点，是迄今为止最稳定的控制方法。
它所涉及的参数物理意义明确，理论分析体系完整并为工程界所熟悉，因而在工业过程控制中得到了广泛应用.从实际需要出发一种好的PID控制器参数整定方法：不仅可以减少操作人员的负担，还可以使系统处于最佳运行状态。
因此，对PID控制器参数整定法的研究具有重要的实际意义。
本文分析了传统的模拟和数字PID控制算法，并对传统的PID控制算法进行微分项和积分项的改进学习了几种比较普遍运用的方法:如不完全微分PID控制算法、微分先行、遇限消弱积分PID控制算法等。
在学习的基础上，提出了一种自整定参数的专家模糊PID控制算法。
由仿真结果可以看到，这种参数自整定方法与一般控制方法抗积分饱和控制法相比,在调节时间、抑制超调量、稳定性都要好,可以在工业上推广使用。
关键词: PID控制结构简单鲁棒性控制算法参数整定Research of the PID Control Arithmeticof Using MATLABAbstractSoftware Abstract So far， the PID is the most common control arithmetic。
Its structure is simple and easy to implement， however，the control effect is perfect and it has a strong robust characteristics。
The physical parameters is, meaning of ，theoretical analysis of system is integrity， and it is familiar by the engineering sector, which in the industrial process control has been widely used. For the actual needs, a good parameter PID controller tuning method can not only reduce the burden on operators， but also make the system running at best. Therefore, the fixed PID controller parameter tuning study has important practicalsignificance。
PID流量控制
一种基于PLC的PID流量控制设计在工业生产过程中对液体流量的高精度控制是不可少的.随着工业技术的不断发展,原有的控制手段已经不能满足对液体流量高精度,高速度的控制需求.在实际工作中采用三级构成:上位机采用工业PC机,其工作稳定,抗干扰能力强,寿命长;PLC部分采用西门子的S7-300系列处理器;外加一块FM355C专用PID控制模块进行数据模块进行数据采集和处理.上位机与PLC之间采用PROFIBUS通讯协议.采用一款西门子的触摸屏与PLC联机用于现场操作[1].1PLC控制系统设计本系统由上位机,PLC,触摸屏,流量计,电动阀构成,系统结构如图1所示.1.1上位机由工业PC机构成,其组态软件采用国产的MCGS6.0,对流量、阀位及其他各种参数进行显示和控制.上位机与PLC采用PROFIBUS通讯协议,最高通讯速率可达到1.5 Mb/s.1.2 PLC控制器PLC控制器包括PS-200,2A电源,CPU314,FM355C模块[2]. FM355C模块的接线端子表如表1.4、5脚为反馈信号输入脚,与靶式流量计连接,对于两线制的流量计4、5引脚间还需接一个10K的负载电阻.8、9为模拟量输出脚,与电动调节阀相连.14、15及18、19脚为第二路PID的输入与输出.1.3传感器和动作机构流量采集采用数字靶式流量计,该种流量计采用累计计数的工作方式,1 s钟累计1次,工作范围40~1 000 L/h,对大流量的采集较为精确.V型调节球阀利用球芯转动与阀座打开相割打开面积(V形窗口)来调节介质流量,调节性能、自洁性能好,适用于高粘度、悬浮液、纸浆告示不干净、含纤维介质场合.采用直连方式与执行机构连接,具有结构紧凑、尺寸小、重量轻、阻力小、动作稳定可靠等优点.流量计和调节阀的信号范围为4~20 mA,与PLC连接.1.4触摸屏采用西门子的TD100触摸屏,与PLC通过PROFIBUS总线相连.使用PROTOOLS6.0编辑界面监控各种参数.当上位机出现故障时,触摸屏可替代上位机操作,提高了系统的可靠性.2PID算法当被控对象的结构和参数不能完全掌握,或得不到精确的数学模型时,控制理论的其他技术难以采用时,系统控制器的结构和参数必须领先经验和现场调试来确定,这时应用PID控制技术最为方便,即利用比例、积分、微分计算出控制量进行控制[3].PID控制为3环节叠加,公式为:m(t) = Kpe(t)+Kpτde(t)dt+KiTi∫i0e(t)dt,其中Kp为比例系数,Kd为微分系数,τ为微分时间常数,Ki为积分系数.对于离散系统的PID公式为:P(k) = Kp{E(k)+TTi∑kj=0E(j)+TdT[E(k)-E(k-1)]}.3配置PID程序模块对西门子的PLC采用SIMA TIC STEP7 V5.3编程.进入STEP7的编程环境后首先通过“工程向导”配置硬件和网络参数,选用的电源模块为PS-200 2A ,中央处理器为CPU 314IFM,PID控制器为FM355 C型.完成配置后打开OB1主程序块(图2),调用FB31模块(STEP7中有LAD,STL ,FBD三种编程方式,STL为语句表编程方式,其他两种为图形调用方式).其中DB31为分配给FB31的背景块.FB31有如下几个参数必须设置:COM_RST参数地址DB31.DBX44. 0 BOOL型. FM355的启动开关.CHANNEL参数地址DB31.DBX2.0 BOOL型.控制端的通道号(每块FM355含两个通道).LMN_RE参数地址DB31.DBX52BOOL,参数类型为REAL型(32位浮点数).存储的是在未启动PID控制时的阀位值(即手动控制值),取值范围为0~100(系统将默认这些数值为电动阀的开度百分数),该参数在PID控制启动后不起作用.LMN_REON参数地址DB31.DBX6.4,参数类型BOOL型(在STEP7中为1位二进制数).当为1时PID控制关闭,LMN_RE的值作为输出值送给电动调节阀.当为0时,PID控制超作用,LMN_RE无效.PHASE参数地址DB31.DBD4,参数类型INT型(16位无符号整数).PID的相位控制,为1时控制相位反向180°.SP_RE参数地址DB31.DBD48,参数类型REAL型.PID 控制的设定值,取值范围为0~100(%).PID控制启动后模块通过计算该值与采集值的差值ER来改变输出值,仅当LMN_REON为0时有效.DEADB_W参数地址DB31.DBD104,参数类型REAL型.默认值为0,单位值为0,单位Hz.不工作区带宽设定值,差值ER将通过这个参数滤波.它关系到PID控制的性湖北大学学报(自然科学版)第28卷能.GAIN参数地址为DB31.DBD108,参数类型为REAL型.默认值为1.增益控制值,增益过大会提高系统的趋近速度,但同时会增大系统波动,导致系统不稳定.增益过小则会使系统的趋近速度变慢.TI参数地址为DB31.DBD112,参数类型REAL型.默认值为3000,单位s.积分时间常数.TI=0时,无积分环节.TD参数地址为DB31.DBD116,参数类型REAL型.默认值为0,单位s.微分时间常数.TD=0时,无微分环节.TM_LAG参数地址为DB31.DBD120,参数类型REAL型.默认值为5,单位s.微分时间延迟设置.LOAD_PAR参数地址为DB31.DBX44.3,参数类型为BOOL型.PID控制的启动开关,每次启动PID或改变PID参数后必须将此位置1,系统每次检查到此位为1,则将所有参数下载到FM355模块,然后将此位复位[4].4PID参数的调整方法PID参数的设置一方面是要根据控制对象的具体情况而定;另一方面是经验.Kp可控制幅值震荡,Kp大则会出现幅值震荡的幅度大,但震荡频率小,系统达到稳定时间长;Ki是解决动作响应的速度快慢的,Ki大了响应速度慢,反之则快;Kd是消除静态误差的,一般Kd设置都比较小,而且对系统影响比较小[5].5试验结论本系统在葛店的新武大有机硅厂通过测试.测试中流体采用甲醇,测试范围为50~250 L/h.以设定值为200 L/h的系统阶跃响应曲线为例,系统延迟时间td=3.5 s,上升时间tΓ=5 s,峰值时间tp=7 s,调节时间ts=28.5 s,超调量公式为:δ%=h(tp)-h(∞)h(∞)×100%,在试验中h(tp)=290,h(∞)=200,所以δ%=45%.经过调试后,本系统被证明完全能胜任有机硅生产过程中,对甲醇流量的精确控制. 参考文献:[1]周军,海心.电气控制及PLC[M].北京:机械工业出版社,2001:90 135.[2]余雷声,方宗达.电器控制与PLC应用[M].北京:机械工业出版社,1999:126 152.[3]陶永华,尹怕欣,葛芦生.新型PID控制及其应用[M].北京:机械工业出版社,1998.[4] Kember G, Dubay R. PID gain scheduling using fuzzy logic[J]. JSA Transactions, 2000,39(3):317 325.[5] Liu G P, Daley S. Optimal-tuning nonlinear PID controllers for unstable processes based on gain and phase marginspecifications: a fuzzy neural approach[J]. Fuzzy Sets and Systems, 2002: 128(1):95 106.The PID control system of flow measuring based on PLCXIAO Lei, XIE Ju-fang(School of Phisics and Electronic Technology, Hubei University, Wuhan 430062, China) Abstract:Described a kind of PID control system based on PIC. The system is composed of supervisor PC, PLC control block, the touch screen ,the flow measuring probe and the electrically operated valve. The system reads the flow measuring from the probe and then calculate the output value by using PID algorithm. The value output to the electrically operated valve to control the flow.The system also can be used in hard condition with high quality.Key words:flow measuring control system; PID; PLC; FM355C(责任编辑晏建章)。
小型无人直升机的系统设计(中英文翻译)
SYSTEM DESIGNING FOR A SMALL-SCALEAUTONOMOUS HELICOPTERThis paper presents the design of a relative low-cost and more compatible autonomous helicopter system using HIROBO 50 scale as an experimental platform. Because of the limit of helicopter payload, we choose the MP2128 Autopilot and a number of sensors to build the system and the weight of instrumentation is about 500 g, much less than the payload capability of model helicopter. Thus it is feasible to design the binocular stereo-camera system to achieve full autonomous flight and the whole weight (include power)of instrumentation is about 1500 g. After getting the model of the helicopter using the subspace model identification (SMI) algorithms, we present the structure of fuzzy PID controller. Keywords: Helicopter; MP2128; MOESP; Fuzzy PID controller.1. IntroductionOver the past decade, small-scale helicopters are increasingly popular platforms for unmanned aerial vehicles (UA Vs). As helicopters have unique flight capability (for example: Low-speed flight, hovering flight, taking off and landing vertically and their agility, etc), it can offer a useful platform for a number of special flight missions such as surveillance, rescue, security monitoring, photography, etc. There are many autonomous helicopters which have been developed for aerial applications differently [Amidi et al., 1998;Conway, 1995]. Roberts et al. presented a small autonomous helicopter which requires noground-to-helicopter communications unless in the event of an emergency [Roberts et al.,2001]. Normally, the onboard instrumentations are designed differently for different missions in a way.In this paper, we presents a relative low-cost and more compatible autonomous helicopter system using HIROBO 50 scale radio-controlled helicopter equipped with a number of more compatible onboard instrumentations. The initial aim of this project is to develop an unmanned helicopter system which can fly autonomously. The further goal of this research is to achieve soft-landing on a moving target autonomously.In order to fulfill the flight mission, Instrumentations onboard of the helicopter are necessary to measure the flying data of helicopter and control its velocity, position and attitude, as well as to communicate with the ground control software system. But the payload of HIROBO 50 scale radio-controlled helicopter is about 2 kg. This makes the system designing onboard more significant.This paper is organized as follows: In Sec. 2, we introduce the model helicopter and MP2128-UA V. In Sec. 3, we present the configuration of sensors. In Sec. 4, the structure of control system of the unmanned helicopter is introduced. In Sec. 5, we describe the communication strategy of system. Finally, in Sec 6, we calculate the whole payload of instrumentation and draw some conclusion.2. Configuration of the SystemYamaha R50 model helicopter is the perfect choice for many research groups because of its adequate payload (about 20 kg) and reliable operation. But it is rather more expensive for our research group. The helicopter we chosen is a relative low-cost, radio-controlled helicopter— HIROBO 50 scale helicopter as flight platform which equipped with autopilot component— MicroPilot Autopilot MP2128-UA V and a number of sensors.2.1. The helicopterHIROBO 50 scale model helicopter, shown in Fig. 1, was chosen as an experimental platform [Chen et al., 2006]. This model helicopter is a commercially available small-size helicopter. As with other small-size helicopter, HIROBO 50 has two blades of the main rotor which generate the needed to lift the helicopter. Because of the small size and relative fast rotor speed, it is fitted with a control rotor to add damping in order to lower the dynamics of the system. The control rotor also reduces the power needed by the actuators to control the helicopter [HIROBO Limned, 2003]. Its parameter is as follows: Helicopter type: HIROBO Shuttle SCEADUE volution 50, rotor diameter: 1350mm, gross weight: 3.23 kg, gear ratio: 8.7:1:4.71Engine type: OS 50 class engine Payload: about 2 kg.Fig. 1. HIROBO 50 scale model helicopter.2.2.ServosNormally, there are five servos which act as inputs to pilot the model helicopter in the small-scale autonomous helicopter:• Elevator (longitudinal cyclic pitch)• Aileron (lateral cyclic pitch)•Collective (main rotor blade pitch)• Rudder (tail rotor blade pitch)• Engine throttle.Accordingly, the outputs which fulfil to control the helicopter’s behavior are pitch control, roll control, up/down control, yaw control and engine revolutions per minute control. In HIROBO 50 scale helicopter, the throttle servo and collective servo are mixed and there are four inputs actually. The servos receive signals from Micro Pilot Autopilot which is mounted in the model helicopter when it flies autonomously, or from R/C when manually.2.3. MP-2128 UAV-FBThe autopilot system, shown in Fig. 2, is produced by MicroPilot Corporation. It is consisted of MP-2128g [MicroPilot Corporation, 2006], ultrasonic sensor, wireless video camera system, and 2.4 GHz standard range data-link. The MP-2128g is the main component of all. It is designed for fully autonomous operation and can provide flight speed, flight altitude, and GPS navigation. There are PIC(pilot in command mode) and CIC (computer in command mode) which can be switched by Channel 5. There are 12 feedback loops that can be selected by control system to fly UA V. All feedback loops gains of PID and flight parameters are adjustable in flight. Significantly, the MP 2128g core is only 28g and this is the sound reason for us to choose it.There are two methods that can be used to adjust settings of the fields on MisroPilot Autopilot: The HORIZONmp ground control software [MicroPilot Corporation, 2006] and HyperTerminal included with all version of Windows via the standard RS232 serial link. When equipped with the HORIZONmp ground control software running in portable computer shown in Fig.3, the autopilot system provides flight mission creation, flight parameter adjustment, flight monitoring as well as an extensive internal data logging that can be used to analyze flights.Fig. 2. MicroPilot autopilot system.Fig. 3 The HORIZONmp running in portable computer.3.Configuration of Sensors3.1. MP2128g boardIn order to measure the helicopter’s attitude,position and velocity, certain sensors are needed. GPS, gyros, airspeed pressure transducer and altitude pressure transducer are intergraded on the MP2128g board as shown in Fig. 4 [MicroPilot Corporation, 2006].The Gyro provides roll rate and yaw rate of the helicopter. MP 2128 board includes an integrated GPS receiver using the Trimble TSIP protocol. The GPS provides position, velocities of the helicopter.Since the altitude pressure transducer cannot detect the relative height, an AGL sensor is required for autonomous runway takeoff and landing. The AGL is an ultrasonic altimeter that provides altitude information up to 16 feet above the ground. The AGL board is connected to the P2 connector on MicroPilot Autopilot.For helicopter has the capability of hover, Compass is needed to provide the azimuth position. The compass module is a three-dimensional compass that can compensate for pitch and roll. Use the compass module in applications where GPS headings are inaccurate or unreliable, such as [MicroPilot Corporation, 2006].• In a hovering aircraft which cannot use the GPS for direction when hovering• In slow moving aircraft, like a blimp, in which GPS headings are unreliable• For dead reckoning if the GPS is lost• When operating the autopilot in strong winds.The electronic compass has a double sided connector which connects to the expansion connector(P3) on the autopilot board with an expansion cable.Fig. 4 MP2128g board.3.2. Vision systemThe helicopter will be equipped with binocular stereo-camera system later based on PC 104 and PC 104-Plus cards shown in Fig. 5 and consists of:• a PC104-Plus Profive-CPU-P5 motherboard with an Intel Pentium M1.6G processor • a PC104-Plus Profive Ethernet board• a PC104-Plus Profive VGA card• a Tri-M PC-104 power supply• a set of radio transmitter/receiver communicates with MP2128 UA V• Binary-cameras to orient the moving target.The vision processing software runs under the on-board real-time operating system, RT-Linux, and uses a custom streaming video driver for the frame-grabber. The flow chart ofstereo-image processing was shown in Fig. 6.The vision system provides the surface information of the landing position. Furthermore, it can identify the moving target and track it. The vision system was described in [Zhu et al., 2006] in detail.Fig. 5 The stereo vision based on PC 104-plus.Fig. 6 Flow chart of stereo-image processing.3.3. Force sensorsSince the attitude of moving target is uncertain, the force sensors are needed for helicopter soft-landing. Here, we want to choose the conductive rubber as force sensor. When the conductive rubber is pressured, its resistance will change. The relation between conductive rubber resistance and pressure is shown in Fig. 7 [Jin et al.,1997]. According to the circuit like Fig. 8 [Tian et al., 2004], we can get the relation between pressure and output voltage as Eq. (1):u = f(Rp) = g(P). (1)The A/DC board mounted on the helicopter then sample the signal and transmit it to force value. There are four conductive rubber force sensors which be mounted on the modelhelicopter(shown in Fig. 9)Fig. 7 The relation between conductive rubber resistance and pressure.Fig. 8 Measurement of the conductive rubber resistance.Fig. 9 The landing gear set with force sensorsIn Fig. 9, the landing gear supplied with model helicopter is made of aluminium tube.It can be certain of relative relation between the attitude of the model helicopter and the landing area according to the four force sensors for the landing area is not always the same level of horizontal surface as in Table 1.Table 1. The situation of force sensorsIn Table 1, “1” means this force sensor has an output value, and “0” has no output value. The control strategy in these nine situations are different each other. We can change some of the four inputs to adjust the attitude and velocities of helicopter in order to achieve soft-landing properly. For example, in situation 1, the model helicopter’s attitude is pitching backward and rolling left side, so inputs of lateral control and pitch control must adjust tomaintain the helicopter’s behavior.4. Control SystemBecause most multivariable control methods are model-based, and the dynamic model for a particular small-scale helicopter which is simple enough to be practical for controller, is not readily available, the identification of the small-scale helicopter‘s dynamic model is a necessary part of any model-based control design.4.1. Small-scale helicopter identification using MOESPAs it is described in [Hashimoto et al., 2000],a problem of importance for the flight control of autonomous helicopter is its inherent qualities: The dynamics of the helicopter are essentially unstable, there are nonlinear variations in dynamics with air speed. Moreover, the helicopter has six degrees of freedom in its motions which is multi-input multi-output system and its flight modes are cross-coupled. Usually, the helicopter can be modeled as a linear system around trim points and there are several examples of the application of system identification techniques to the modeling of small-scale helicopter [Tischler et al., 1996; Morris et al., 1994].Subspace Model identification (SMI) algorithms are a group of methods that identify a MIMO state-space system using numerically robust computation tools such as QR factorization and SVD (singular value decomposition).Subspace methods rely on the ideas of stochastic realization and have been developed by many researchers [Chiuso, 2001; Akaike, 1974;Van Overschee, 1996]. In contrast to the classical approaches of maximum likelihood or PEM methods, the subspace method avoids the use of canonical forms. The MOESP (multivariable output error state space) identification method has already been applied to some modeling the identification of a test satellite [Adachi, 1999] and BO 105 helicopter [Verhaegen, 1995].We had applied the MOESP method in the model identification simulation of a small-scale helicopter dynamics using MATLAB to find a model of the helicopter in hover using the collective pitch and rudder inputs as Eq. (2):In order to test this algorithm’s validity, we use the sequence consisting of the Gaussian, white noise signal with 4000 data points as the inputs of the system model obtained by MOESP and PEM. We compare the outputs error using this method with that of the PEM in Eq. (3) and find that the result of using MOESP method is more accurate than that of the PEM method. Moreover, it does not need any priori information about the identifying model:er11 = 1.5062e −022, er21 = 1.7125e −023, er31 = 9.4457e −018;er12 = 153.1331, er22 = 2.4398e + 004, er32 = 3.9928e + 007.Here, the Index k is the length of data points,y i1 y'i1(i = 1, 2, 3) are the outputs error of the real system to the result of using MOESP and,Y i2−y'i2(i = 1, 2, 3) are the outputs error of the real system to the result of using PEM..4.2. Fuzzy PID controllerThere are many different types of controller for an unmanned helicopter [Kadmiry et al., 2001;Jensen et al., 2005; Shim et al., 1998; Phillips et al., 1996]. To control the helicopterdifferent advanced control methods could be used which have their own advantages and disadvantages and the most significant for this project will be discussed and a conclusion will be made. The robust controller [Clausen et al., 2001] is considered a good controller if there are uncertainties on the parameters and disturbances in a MIMO system to be dealt with apart from the complexity computation of matrix. Optimal control is mostly used to optimize a given controller, often MIMO, to give the control profile a power optimal, time optimal, jerk optimal, or a combined optimal profile, and can be extended to a robust optimal controller [Jensen et al., 2005].A classical fuzzy logic controller is a knowledge-based system and is easy to construct because it has no need of a model of the system. Often it is used in control of nonlinear systems and systems where the parameters are hard to determine. But they are hard to tune, and the accuracy of outputs usually cannot be guaranteed if the number of rules are small. On the other hand, the number of rules increases exponentially with the number of inputs and it makes this classic fuzzy logic controller becoming impractical.Neural network controller has its primary advantage with highly nonlinear systems. It can “learn” a system’s transfer function from training data and function. It is not to be suitable for unmanned helicopter control, since it has no explicit expression for the inputs and outputs and the necessary parameters for training will not be at hand.Fig. 10 Vertical climb manoeuvrers using (A) nonlinear,(B) fuzzy and (C) robust control for the (1) nominalmodel and for uncertainties in (2) system weight.Fig. 11 The block diagram of Fuzzy PID controller for small-scale unmanned helicopter.Shim and Koo et al. had test that the performance of the controllers designed by using linear robust multi-variable control, fuzzy logic control with evolutionary tuning, and nonlinear tracking control in regard to disturbance rejection, uncertainties in system parameter and tracking accuracy [Shim et al., 1998], and obtained the conclusion shown in Fig. 10 that the robust and fuzzy controllers are capable of handling uncertainties and disturbances and the nonlinear control covers a substantially wider range of flight envelopes, but requires accurate knowledge about the system for vertical climb. Taking into account of the capability of payload of the unmanned helicopter and the complexity of computation, we develop our control system using Fuzzy PID controller shown in Fig. 11. According to the paper [Phillips et al.,1996], the fuzzy logic controller is composed of four sections: the longitudinal cyclic control, later cyclic control, rudder control, and collective control. In order to decrease the steady state errors between the real outputs and the desired outputs, we design the PID controller after fuzzy logic controller. This control system takes advantage of the merits of these two controllers.5. CommunicationThe communication system is shown in Fig. 12[MicroPilot Corporation, 2006]. The pilot can control the helicopter by RC transmitter/receiver manually and can also switch it to fly autonomously by pushing a select channel. The portable computer running the HORIZONmp on the ground communicate with the MP2128-UA V, which mounted on the helicopter by RF model which frequency is 2.4GHz, data rate is 9600 bps. The outdoor/RFline-of-sight rang of radio model is up to 16 km,indoor/urban range is up to 180m and receiver sensitivity at 9600bps is −105 dBm.Fig. 12 Communication system of small-scale autonomous helicopter.There is aCOM connector which can be used to copy flight data of the helicopter such as position, velocities,attitude, altitude and landing force from MP2128-UA V to portable computer for analyzing and resolving problems.6. ConclusionDue to our desire to design a small-scale helicopter which can achieve soft-landing on a moving target, we present the configuration of the unmanned helicopter system which has a large number of sensors located on it in order to measure the attitude, velocity and position of the helicopter and other certain information.For the small-scale autonomous helicopter has limited payload, we choose the MP2128-UA V,HORIZONmp ground control software and some sensors to design autopilot system. The whole weight (include power) of instrumentation is about 1500 g (the whole weight of MP2128-UA V, compass, AGL, and force sensors is within 500 g, stereo vision system based on PC 104-plus is about 1000 g), less than the payload capability of HIROBO 50 scale model helicopter. Now,we are using the XTENDER Software Development Kit [MicroPilot Corporation, 2006] to develop our own flight mission to run with MicroPilot autopilot code and ground control software.小型无人直升机的系统设计本文介绍了设计一个相对低成本和一个更兼容的无人直升机系统，该系统以HIROBO50作为实验平台。
主动悬架控制理论综述
Control Systems in Active or Semi-active SuspensionV. Sankaranarayanan in Semi-active Suspension Control of a Light Commercial Vehicle, designed a hybrid controller with a skyhook and ground hook. The suspension velocity was also predicted by using Kalman filter in order to faster reach a desired effect of the controller. The controller was implemented in an actual vehicle equipped with the continuously varying dampers.Savaresi in An Optimal Control Algorithm for Comfort-oriented Semi-active Suspension, developed the Acceleration-Driven-Damper control strategy to 1/4 vehicle model.D. Sammier’s Skyhook and H∝Control of Semi-active Suspension: Some Practical Aspects aimed at dffdctive control of road holding which applied H∝theory and skyhook theory.K.S. Hong in Modified skyhook Control of Semi-active Suspension: A New Model Gain Scheduling and Hardware-in-the-loop Tuning, developed a road adaptive modified skyhook control upon the requirement of using only one acceleration sensor.D.C. Batterbee in Hardware-in-the-loop Simulation of Magnetorheological Dampers for Vehicle Suspension System, designed a linearized modified skyhook control strategy by HILS, testing of two-degree-of-freedom structure.W.T.Sie in Enhancing Gery Prediction Fuzzy Controller for Active Suspension System, proposed gray prediction fuzzy logic (GPFC) scheme to control an active suspension. The GP can predict the signal variation using discrete time-sequence data to establish an ordinary differential equation.K.G. Sung in Vibration Control of Vehicle ER Suspension System Using Fuzzy Moving Sliding mode Controller, designed a new moving sliding mode controller (MSMC). Fuzzy sliding mode controller (FSMC) has excelled in dealing with system that are complex, ill-defined, nonlinear or time-varying.The self-organizing fuzzy sliding mode controller (SOFSMC) contains FSMC and the organizing learning mechanism for ensuring stability and consistent performance.A hybrid application of such control systems above can be found in Effect of a New Hybrid Controller under Random Road Excitation for Semi-active Suspension System.。
基于模糊PID算法的半桥DC／DC控制器的优化设计
础 上 ．叙述 了模 糊 ＰＩＤ控 制 系 统 的基 本 原 理 、设 计 过 程 以及 控 制 理 论 的优 越 性 ；通 过 Ｓｉｍｕｌｉｎｋ软 件 仿 真 和 硬 件 电路 实 验 ，对 比模 糊 ＰＩＤ控 制 器 与 增 量 式 ＰＩＤ控 制 器 的实 验 控 制 结 果 ，验 证 了 所 提 优 化 控 制 方 法 的 可 靠 性和 有 效 性 。
有 改 善 控 制 系 统 的稳 定 性 和 自适 应 性 的 能 力 。
关键词 ：控 制器 ：电压 反馈 ：半桥 ＤＣ／ＤＣ变 换器
中 图分 类 号 ：ＴＭ５７１
文 献 标 识 码 ：Ａ
文 章编 号 ：１０００一ｌＯＯＸ（２０１８）０９—００７４－０４
Ｏｐｔｉｍａｌ Ｄｅｓｉｇｎ ｏｆ Ｈａｌｆ－ｂｒｉｄｇｅ ＤＣ／ＤＣ Ｃｏｎｔｒｏｌｌｅｒ Ｂａｓｅｄ ｏｎ Ｆｕｚｚｙ Ｐｍ Ａｌｇｏｒｉｔｈｍ
１ 引 言
由 于 ＶＣＨＢ ＤＣ／ＤＣ变 换 器 是 一 个 强 非 线 性 系 统 ，采 用 传 统 的线 性控 制 方 法 实 现 对 ＤＣ／ＤＣ变 换 器 的控 制 ．存在 诸 多 的局 限性 。例 如 。典 型 的增 量 式 ＰＩＤ控 制 算 法 ，只 能 满 足 系 统 在 线 性 工 作 点 附近 的 工 作 要 求 ．对 于 ＶＣＨＢ ＤＣ／ＤＣ变 换 器 这 种 具 有 强非 线 性 系 统 变 化 特 征 的 控 制 效 果 往 往 不 够 理 想 ．尤 其 是 在 变 负 载 和 输 入 电压 存 在 外 部 扰 动 的情 况 下【“
为 提 升 ＶＣＨＢ ＤＣ／ＤＣ变 换 器 在 非 线 性 区 的 输 出 性 能 。文 献 ［２—４］提 出 了相 应 控 制 方 法 ，在 一 定程 度 上提 高 了控 制 系 统 的输 出性 能 和 稳 定 性 ， 但 基 本 都 停 留在 理 论推 导 和 仿 真 实 验 的 阶 段 ，缺 少硬 件 电路 实验 验 证 。
一阶惯性环节的模糊PID自整定控制算法的设计
= K P e ( k ) + K i ∑ e ( j ) + K d [ e ( k ) − e ( k − 1)]
j =0
k
比例环节作用在于加快系统的响应速度 节精度 越高 Kp 越大 系统的响应速度越快 也就是对误差的分辨率越高
提高系统调 甚
K
P
为比例系数
Ki = K P
T Ti
为积分系数
系统的调节精度
万方数据
第 15 卷第 2 期 的控制规则表如表 2 所示 表２
Ki E NB NM NS O PS PM PB NB NB NB NM NM O O NB NB NM NM NS O O NM NM NS NS O PS PS NM NS NS O PS PS PM NS NS O PS PS PM PM E NB NM
常将积分环节分离出来 调 KI 取零值
率 在 0 值附近的函数形状取得更陡一些 形式如图 2 所 示 各变量的模糊量分别为 E 46 EC Kp KI KD 其模糊集合的论域为[-6 -5 -4 -3 -2 -1 0 1 2 3
入积分环节 因此 当偏差｜ｅ｜大或较大时 为避免系统超 当｜ｅ｜较小时 积分环节有效 随｜ｅ｜的减 KI 小而增大 以消除系统的稳态误差 提高控制精度
控制效果 数 中
PID 调节器便可实现作用
大多数工业过程都不同程度地存在非线性
性和模型不确定性 因而一般的 PID 控制无法实现对这样 过程的精确控制 型 由于模糊控制不需要建立过程的精确模 针对 PID 控制和模糊
所以得到了越来越广泛的应用
控制的各自特点
国内外学者分别采用不同的方法将模糊 研究出了多种模糊 PID 控制
但容易产生超调
模糊PID控制及其MATLAB仿真讲解
模糊PID控制及其MATLAB实现姓名：专业班级：学号：授课教师：摘要PID（比例积分微分）控制具有结构简单、稳定性能好、可靠性高等优点,尤其适用于可建立精确数学模型的控制系统。
而对于一些多变量、非线性、时滞的系统，传统的PID控制器并不能达到预期的效果。
随着模糊数学的发展，模糊控制的思想逐渐得到控制工程师们的重视，各种模糊控制器也应运而生。
而单纯的模糊控制器有其自身的缺陷—控制效果很粗糙、控制精度无法达到预期标准。
但利用传统的PID控制器和模糊控制器结合形成的模糊自适应的PID控制器可以弥补其缺陷；它将系统对应的误差和误差变化率反馈给模糊控制器进而确定相关参数，保证系统工作在最佳状态，实现优良的控制效果。
论文介绍了参数自适应模糊PID控制器的设计方法和步骤。
并利用MATLAB 中的SIMULINK 和模糊逻辑推理系统工具箱进行了控制系统的仿真研究，并简要地分析了对应的仿真数据。
关键词: 经典PID控制; 模糊控制; 自适应模糊PID控制器; 参数整定; MATLAB仿真ABSTRACTPID(Proportion Integration Differentiation) control, with lots of advantages including simple structure, good stability and high reliability, is quite suitable to establish especially the control system which accurate mathematical model is available and needed. However, taken multivariable, nonlinear and time-lag into consideration, traditional PID controller can not reach the expected effect.Along with the development of Fuzzy Mathematics, control engineers gradually pay much attention to the idea of Fuzzy Control, thus promoting the invention of fuzzy controllers. However, simple fuzzy controller has its own defect, where control effect is quite coarse and the control precision can not reach the expected level. Therefore, the Fuzzy Adaptive PID Controller is created by taking advantage of the superiority of PID Controller and Fuzzy Controller. Taken this controller in use, the corresponding error and its differential error of the control system can be feed backed to the Fuzzy Logic Controller. Moreover, the three parameters of PID Controller is determined online through fuzzification, fuzzy reasoning and defuzzification of the fuzzy system to maintain better working condition than the traditional PID controller.Meanwhile，the design method and general steps are introduced of the Parameter self-setting Fuzzy PID Controller. Eventually, the Fuzzy Inference Systems Toolbox and SIMULINK toolbox are used to simulate Control System. The results of the simulation show that Self-organizing Fuzzy Control System can get a better effect than the Classical PID controlled evidently.Keywords: Classic PID control; Fuzzy Control; Parameters tuning; the Fuzzy Adaptive PID Controller; MATLAB simulation目录第一章绪论 (1)1.1 研究的背景及意义 (1)1.2 经典PID控制系统的分类与简介 (2)1.2.1 P控制 (2)1.2.2 PI控制 (2)1.2.3 PD控制 (2)1.2.4 比例积分微分（PID）控制 (2)1.3 模糊逻辑与模糊控制的概念 (3)1.3.1 模糊控制相关概念 (3)1.3.2 模糊控制的优点 (4)1.4 模糊控制技术的应用概况 (4)1.5 本文的研究目的和内容 (5)第二章PID控制 (6)2.1 PID的算法和参数 (6)2.1.1 位移式PID算法 (6)2.1.2 增量式PID算法 (7)2.1.3 积分分离PID算法 (7)2.1.4 不完全微分PID算法 (8)2.2 PID参数对系统控制性能的影响 (9)2.2.1 比例系数K P对系统性能的影响 (9)2.2.2 积分时间常数T i对系统性能的影响 (9)2.2.3 微分时间常数T d对系统性能的影响 (9)2.3 PID控制器的选择与PID参数整定 (10)2.3.1 PID控制器的选择 (10)2.3.2 PID控制器的参数整定 (10)第三章模糊控制器及其设计 (11)3.1 模糊控制器的基本结构与工作原理 (11)3.2 模糊控制器各部分组成 (11)3.2.1 模糊化接口 (11)3.2.2 知识库 (12)3.2.3 模糊推理机 (12)3.2.4 解模糊接口 (13)3.3模糊推理方式 (13)3.3.1 Mamdani模糊模型（迈达尼型） (13)3.3.2 Takagi-Sugeno模糊模型(高木-关野) (13)3.4模糊控制器的维数确定 (14)3.5 模糊控制器的隶属函数 (15)3.6模糊控制器的解模糊过程 (17)3.7 模糊PID控制器的工作原理 (18)第四章模糊PID控制器的设计 (19)4.1 模糊PID控制器组织结构和算法的确定 (19)4.2 模糊PID控制器模糊部分设计 (19)4.2.1 定义输入、输出模糊集并确定个数类别 (19)4.2.2 确定输入输出变量的实际论域 (20)4.2.3 定义输入、输出的隶属函数 (20)4.2.4 确定相关模糊规则并建立模糊控制规则表 (20)第五章模糊PID控制器的MATLAB仿真 (24)5.1 模糊PID控制的仿真 (24)5.1.1 FIS编辑器 (24)5.1.2 隶属函数 (25)5.1.3 模糊规则库 (25)5.2 对模糊控制器编程仿真 (27)第六章结语 (31)参考文献 (32)第一章绪论1.1 研究的背景及意义随着越来越多的新型自动控制应用于实践，其控制理论的发展也经历了经典控制理论、现代控制理论和智能控制理论三个阶段。
PID Controllers - Theory Design and Tuning 翻译
IntroductionThe PID controller has several important functions: it provides feed¬back; it has the ability to eliminate steady state offsets through in¬tegral action; it can anticipate the future through derivative action.PID controllers are sufficient for many control problems, particularly when process dynamics are benign and the performance requirementsare modest. PID controllers are found in large numbers in all indus¬tries. The controllers come in many different forms. There are stand¬alone systems in boxes for one or a few loops, which are manufac¬tured by the hundred thousands yearly. PID control is an important ingredient of a distributed control system. The controllers are also embedded in many special-purpose control systems. In process con¬trol, more than 95% of the control loops are of PID type, most loopsare actually PI control. Many useful features of PID control have notbeen widely disseminated because they have been considered trade secrets. Typical examples are techniques for mode switches and antiwindup. PID control is often combined with logic, sequential machines, se¬lectors, and simple function blocks to build the complicated automa¬tion systems used for energy production, transportation, and manu¬facturing. Many sophisticated control strategies, such as model pre¬dictive control, are also organized hierarchically. PID control is usedat the lowest level; the multivariable controller gives the setpoints tothe controllers at the lower level. The PID controller can thus be saidto be the "bread and butter" of control engineering. It is an important component in every control engineer's toolbox.PID controllers have survived many changes in technology rang¬ing from pneumatics to microprocessors via electronic tubes, tran¬sistors, integrated circuits. The microprocessor has had a dramatic2 Chapter 1 Introductioninfluence on the PID controller. Practically all PID controllers madetoday are based on microprocessors. This has given opportunities to provide additional features like automatic tuning, gain scheduling,and continuous adaptation. The terminology in these areas is notwell-established. For purposes of this book, auto-tuning means thatthe controller parameters are tuned automatically on demand froman operator or an external signal, and adaptation means that the parameters of a controller are continuously updated. Practically allnew PID controllers that are announced today have some capabilityfor automatic tuning. Tuning and adaptation can be done in many different ways. The simple controller has in fact become a test benchfor many new ideas in control.The emergence of the fieldbus is another important development.This will drastically influence the architecture of future distributed control systems. The PID controller is an important ingredient ofthe fieldbus concept. It may also be standardized as a result of the fieldbus development.A large cadre of instrument and process engineers are familiarwith PID control. There is a well-established practice of installing, tuning, and using the controllers. In spite of this there are substantial potentials for improving PID control. Evidence for this can be found in the control rooms of any industry. Many controllers are put in man¬ual mode, and among those controllers that are in automatic mode, derivative action is frequently switched off for the simple reason that it is difficult to tune properly. The key reasons for poor performance is equipment problems in valves and sensors, and bad tuning practice. The valve problems include wrong sizing, hysteresis, and stiction. The measurement problems include: poor or no anti-aliasing filters; excessive filtering in "smart" sensors, excessive noise and improper calibration. Substantial improvements can be made. The incentive for improvement is emphasized by demands for improved quality, which is manifested by standards such as ISO 9000. Knowledge and un¬derstanding are the key elements for improving performance of the control loop. Specific process knowledge is required as well as knowl¬edge about PID control.Based on our experience, we believe that a new era of PID controlis emerging. This book will take stock of the development, assess its potential, and try to speed up the development by sharing our expe¬riences in this exciting and useful field of automatic control. The goal of the book is to provide the technical background for understanding PID control. Such knowledge can directly contribute to better product quality.Process dynamics is a key for understanding any control problem. Chapter 2 presents different ways to model process dynamics thatare useful for PID control. Methods based on step tests are discussed Chapter 1 Introduction 3together with techniques based on frequency response. It is attempted to provide a good understanding of the relations between the different approaches. Different ways to obtain parameters in simple transfer function models based on the tests are also given. Two dimensionfree parameters are introduced: the normalized dead time and thegain ratio are useful to characterize dynamic properties of systems commonly found in process control. Methods for parameter estimation are also discussed. A brief description of disturbance modeling is also given.An in depth presentation of the PID controller is given in Chap¬ter 3. This includes principles as well as many implementation de¬tails, such as limitation of derivative gain, anti-windup, improvement of set point response, etc. The PID controller can be structured in dif¬ferent ways. Commonly used forms are the series and the parallel forms. The differences between these and the controller parameters used in the different structures are treated in detail. Implementationof PID controllers using digital computers is also discussed. The un¬derlying concepts of sampling, choice of sampling intervals, and anti¬aliasing niters are treated thoroughly. The limitations of PID control are also described. Typical cases where more complex controllers are worthwhile are systems with long dead time and oscillatory systems. Extensions of PID control to deal with such systems are discussed briefly.Chapter 4 describes methods for the design of PID controllers. Specifications are discussed in detail. Particular attention is given to the information required to use the methods. Many different meth¬ods for tuning PID controllers that have been developed over the years are then presented. Their properties are discussed thoroughly.A reasonable design method should consider load disturbances, model uncertainty, measurement noise, and set-point response. A drawback of many of the traditional tuning rules for PID control is that such rules do not consider all these aspects in a balanced way. New tuning techniques that do consider all these criteria are also presented.The authors believe strongly that nothing can replace under¬standing and insight. In view of the large number of controllers used in industry there is a need for simple tuning methods. Such rules will at least be much better than "factory tuning," but they can always be improved by process modeling and control design. In Chapter 5 we present a collection of new tuning rules that give significant improve¬ment over previously used rules.In Chapter 6 we discuss some techniques for adaptation and au¬tomatic tuning of PID controllers. This includes methods based on parametric models and nonparametric techniques. A number of com¬mercial controllers are also described to illustrate the different tech¬niques. The possibilities of incorporating diagnosis and fault detection 4 Chapter 1 Introductionin the primary control loop is also discussed.In Chapter 7 it is shown how complex control problems can be solved by combining simple controllers in different ways. The control paradigms of cascade control, feedforward control, model following, ratio control, split range control, and control with selectors are dis¬cussed. Use of currently popular techniques such as neural networks and fuzzy control are also covered briefly.ReferencesA treatment of PID control with many practical hints is given inShinskey (1988). There is a Japanese text entirely devoted to PID control by Suda et al. (1992). Among the books on tuning of PID controllers, we can mention McMillan (1983) and Corripio (1990), which are published by ISA.There are several studies that indicate the state of the art of in¬dustrial practice of control. The Japan Electric Measuring Instrument Manufacturers'Association conducted a survey of the state of process control systems in 1989, see Yamamoto and Hashimoto (1991). Ac¬cording to the survey more than than 90% of the control loops wereof the PID type.The paper, Bialkowski (1993), which describes audits of papermills in Canada, shows that a typical mill has more than 2000 control loops and that 97% use PI control. Only 20% of the control loops were found to work well and decrease process variability. Reasons for poor performance were poor tuning (30%) and valve problems (30%). The remaining 20% of the controllers functioned poorly for a variety of reasons such as: sensor problems, bad choice of sampling rates, and anti-aliasing filters. Similar observations are given in Ender (1993), where it is claimed that 30% of installed process controllers operatein manual, that 20% of the loops use "factory tuning," i.e., default parameters set by the controller manufacturer, and that 30% of the loops function poorly because of equipment problems in valves and sensors.CHAPTER 2Process Models2.1 IntroductionA block diagram of a simple control loop is shown in Figure 2.1. The system has two major components, the process and the controller, rep¬resented as boxes with arrows denoting the causal relation between inputs and outputs. The process has one input, the manipulated vari¬able, also called the control variable. It is denoted by u. The process output is called process variable (PV) and is denoted by y. This vari¬able is measured by a sensor. The desired value of the process variable is called the setpoint (SP) or the reference value. It is denoted by y sp. The control error e is the difference between the setpoint and the process variable, i.e., e = y sp —y. The controller in Figure 2.1 hasone input, the error, and one output, the control variable. The figure shows that the process and the controller are connected in a closed feedback loop.The purpose of the system is to keep the process variable closeto the desired value in spite of disturbances. This is achieved by thefeedback loop, which works as follows. Assume that the system is in equilibrium and that a disturbance occurs so that the process variable becomes larger than the setpoint. The error is then negative and the controller output decreases which in turn causes the process outputto decrease. This type of feedback is called negative feedback, because the manipulated variable moves in direction opposite to the process variable.The controller has several parameters that can be adjusted. The control loop performs well if the parameters are chosen properly. It performs poorly otherwise, e.g., the system may become unstable. The procedure of finding the controller parameters is called tuning.6 Chapter 2 Process ModelsController! Process- 1 -*Figure 2.1 Block diagram of a simple feedback system.i )This can be done in two different ways. One approach is to choose some controller parameters, to observe the behavior of the feedback system, and to modify the parameters until the desired behavior is obtained. Another approach is to first develop a mathematical model that describes the behavior of the process. The parameters of the controller are then determined using some method for control design. An understanding of techniques for determining process dynamics is a necessary background for both methods for controller tuning. This chapter will present such techniques.Static models are discussed in the next section. Dynamic modelsare discussed in Section 2.3. Transient response methods, which are useful for determining simple dynamic models of the process, are pre¬sented in Section 2.4. Section 2.5 treats methods based on moments. These methods are less sensitive to measurement noise and, further¬more, are not restricted to any specific input signal. The frequency response methods, described in Section 2.6, can be used to obtain both simple models and more detailed descriptions. Methods basedon estimation of parametric models are more complex methods that require more computations but less restrictions on the experiments. These methods are presented in Section 2.7. The models discussed so far describe the relation between the process input and output. It is also important to model the disturbances acting on the system. Thisis discussed in Section 2.8. Section 2.9 treats methods to simplify a complex model and the problem of unmodeled dynamics and mod¬eling errors. Conclusions and references are given in Sections 2.10 and 2.11.2.2 Static ModelsThe static process characteristic is a curve that gives the steady state relation between process input signal u and process output y. See Figure 2.2. Notice that the curve has a physical interpretation onlyfor a stable process.2.2 Static ModelsFigure 2.2 Static process characteristic. Shows process output yas a function of process input u under static conditions.All process investigations should start by a determination of thestatic process model. It can be used to determine the range of control signals required to change the process output over the desired range,to size actuators, and to select sensor resolution. It can also be usedto assess whether static gain variations are so large that they haveto be accounted for in the control design.The static model can be obtained in several ways. It can be de¬termined by an open-loop experiment where the input signal is setto a constant value and the process output is measured when it has reached steady state. This gives one point on the process characteris¬tics. The experiment is then repeated to cover the full range of inputs. An alternative procedure is to make a closed-loop experiment.The setpoint is then given a constant value and the corresponding control variable is measured in steady state. The experiment is then repeated to cover the full range of setpoints.The experiments required to determine the static process modeloften give a good intuitive feel for how easy it is to control the process, if it is stable, and if there are many disturbances.Sometimes process operations do not permit the experiments to be done as described above. Small perturbations are normally permitted, but it may not be possible to move the process over the full operating range. In such a case the experiment must be done over a long period of time.Process NoiseProcess disturbances are easily determined by logging the process output when the control signal is constant. Such a measurement8 Chapter 2 Process Modelswill give a combination of measurement and load disturbances. There are many sophisticated techniques such as time-series analysis and spectral analysis that can be used to determine the characteristicsof the process noise. Crude estimates of the noise characteristicsare obtained simply by measuring the peak-to-peak value and by determining the average time between zero crossings of the error signal. This is discussed further in Section 2.8.2.3 Dynamic ModelsA static process model like the one discussed in the previous sectiontells the steady state relation between the input and the output sig¬nal. A dynamic model should give the relation between the input andthe output signal during transients. It is naturally much more diffi¬cult to capture dynamic behavior. This is, however, very significantwhen discussing control problems.Fortunately there is a restricted class of models that can often beused. This applies to linear time-invariant systems. Such models canoften be used to describe the behavior of control systems when thereare small deviations from an equilibrium. The fact that a system islinear implies that the superposition principle holds. This means thatif the input u\ gives the output yi and the input ui gives the outputj2 it then follows that the input au\ + bui gives the output ay\ + by 2-A system is time-invariant if its behavior does not change with time.A very nice property of linear time-invariant systems is that theirresponse to an arbitrary input can be completely characterized interms of the response to a simple signal. Many different signals can beused to characterize a system. Broadly speaking we can differentiatebetween transient and frequency responses.In a control system we typically have to deal with two signalsonly, the control signal and the measured variable. Process dynamicsas we have discussed here only deals with the relation between thosesignals. The measured variable should ideally be closely related to thephysical process variable that we are interested in. Since it is difficultto construct sensors it happens that there is considerable dynamicsin the relation between the true process variable and the sensor. Forexample, it is very common that there are substantial time constantsin temperature sensors. There may also be measurement noise andother imperfections. There may also be significant dynamics in theactuators. To do a good job of control, it is necessary to be aware ofthe physical origin the process dynamics to judge if a good r绪论PID控制器有几个重要的功能: 它能提供反馈; 它有能力通过积分作用消除稳态补偿; 它通过微分作用可以有预见性。
pd舵机控制
pd舵机控制Chapter 1: IntroductionIn recent years, the use of PD (proportional-derivative) control technique in robotics has gained significant attention. PD control is a popular method for controlling servo motors, including the widely used PD servo motors. This paper aims to provide a comprehensive overview of PD servo motor control, its applications, and challenges in modern robotics.Chapter 2: PD Servo Motor Control PrinciplesPD servo motor control is based on the principles of proportional and derivative control. The proportional term provides a direct control on the error signal, while the derivative term helps in predicting the future trend of the error. By combining these two terms, PD control seeks to achieve fast and accurate control of the servo motor.Chapter 3: Applications of PD Servo Motor ControlPD servo motor control finds extensive applications in robotics. It is widely used for precise positioning and motion control tasks, such as robotic arms, automated manufacturing systems, and CNC machines. PD control offers excellent stability and responsiveness, making it suitable for applications that require high precision and quick response.Chapter 4: Challenges and Future DirectionsWhile PD servo motor control has proven to be effective in various applications, it still faces certain challenges. One of the main challenges is the tuning of PD control parameters for optimal performance. The optimization of these parameters can be time-consuming and require expert knowledge. Furthermore, the accuracy of PD control may be affected by various external factors, including environmental conditions and mechanical limitations.To address these challenges, future research directions may focus on the development of advanced control algorithms that can adaptively adjust the control parameters based on real-time feedback. Additionally, new methods for accurate modeling and identification of servo motor dynamics can enhance the performance of PD control. The integration of artificial intelligence techniques, such as neural networks and fuzzy logic, can further improve the robustness and flexibility of PD servo motor control.In conclusion, PD servo motor control is a widely used techniquein robotics for precise positioning and motion control. It offers excellent stability and responsiveness, making it suitable for various applications. However, challenges in parameter tuning and accuracy need to be addressed to further enhance the performance of PD control. Future research directions should focus on the development of advanced control algorithms and integration of artificial intelligence techniques to overcome thesechallenges.Chapter 5: PD Servo Motor Control System Design Designing a PD servo motor control system involves several key steps. The first step is to determine the desired performancespecifications, including the desired settling time, overshoot, and steady-state error. These specifications will guide the selection of suitable control parameters.Next, the servo motor dynamics need to be characterized, including its transfer function and time constants. This can be achieved through experiments or by using mathematical models. The transfer function describes the relationship between the input command and the output position of the motor.Once the motor dynamics are known, the control parameters can be determined. The proportional gain (Kp) controls the strength of the proportional control signal, while the derivative gain (Kd) determines the contribution of the derivative control term. The selection of these gains is crucial, as inadequate values may lead to instability or poor performance. The values of Kp and Kd can be determined through iterative tuning, where the system response is observed and the gains are adjusted until the desired performance is achieved.After determining the control parameters, a feedback loop needs to be implemented. This involves measuring the position or velocity of the motor and comparing it with the desired value. The error signal is then fed into the PD controller, which calculates the control output. This output is applied to the motor to generate the required torque and achieve the desired position or velocity.Chapter 6: Performance Evaluation and OptimizationOnce the PD servo motor control system is implemented, itsperformance needs to be evaluated. Performance metrics such as settling time, rise time, overshoot, and steady-state error can be measured to assess the system's behavior. A step response test, where a step input is applied to the system, is commonly used for performance evaluation. The recorded response can be analyzed to determine if the desired specifications are met.If the system does not meet the desired performance, optimization techniques can be applied. These techniques aim to find the optimal values of the control parameters that minimize the error and improve the system performance. Methods such as gradient descent, genetic algorithms, or particle swarm optimization can be employed for parameter optimization.Chapter 7: Advanced PD Control TechniquesIn recent years, several advanced PD control techniques have been proposed to overcome the limitations of traditional PD control. These techniques aim to enhance the performance, robustness, and adaptability of the control system.One such technique is adaptive PD control, where the control gains are adjusted online based on the real-time measurement of system dynamics. This allows the control system to adapt to changes in the operating conditions or disturbances. Adaptive PD control can improve the accuracy and stability of the system in dynamic environments.Another technique is fuzzy PD control, which incorporates fuzzy logic into the control system. Fuzzy logic allows for linguisticvariables and rules to be used in the control algorithm, which can improve the robustness of the control system by considering uncertainties and varying operating conditions.Neural network-based PD control is another advanced technique that utilizes artificial neural networks to learn the control behavior. Neural networks can learn the complex mapping between the system inputs and outputs, allowing for more accurate control and compensation of nonlinearities.Chapter 8: ConclusionPD servo motor control is a widely used technique in robotics for precise positioning and motion control. It offers excellent stability and responsiveness, making it suitable for various applications. However, challenges in parameter tuning and accuracy need to be addressed to further enhance the performance of PD control.This paper has provided an overview of PD servo motor control principles, applications, challenges, and future directions. The design of a PD control system involves determining the desired performance specifications, characterizing the motor dynamics, selecting control parameters, and implementing a feedback loop. Performance evaluation and optimization techniques can be used to assess and improve the system performance. Advanced PD control techniques, such as adaptive control, fuzzy control, and neural network control, have been proposed to enhance the performance and adaptability of PD control.Overall, PD servo motor control is a fundamental control technique in robotics that continues to evolve and find new applications. Further research and development efforts are needed to overcome the challenges and enhance the performance of PD control in modern robotics systems.。
基于模糊PID的机器人恒力磨抛控制研究
长春理工大学学报（自然科学版）Journal of Changchun University of Science and Technology （Natural Science Edition ）Vol.44No.3Jun.2021第44卷第3期2021年6月收稿日期：2020-01-08基金项目：吉林省科技厅重点技术攻关项目（20190302019GX ）作者简介：王红平（1976-），女，博士，副教授，E-mail ：*****************基于模糊PID 的机器人恒力磨抛控制研究王红平，熊梦强，马国庆（长春理工大学机电工程学院，长春130022）摘要：为了提高工件表面的磨抛质量，提出一种基于模糊PID 的恒力控制算法，以打磨压力误差e 和压力误差变化率e c 为输入变量，将模糊控制与普通PID 结合，对打磨头的压力和位置进行控制。
文中将该方法与传统PID 方法进行仿真对比，并基于FANUC 机器人构建恒力打磨平台，结果表明：该方法具有更快的调节速度、更小的超调量和良好的稳定性，恒力磨抛的工件表面粗糙度在0.18~0.22μm 之间，有效地提高了磨抛质量。
关键词：磨抛质量；模糊PID ；恒力控制；表面粗糙度中图分类号：TP273文献标志码：A文章编号：1672-9870（2021）03-0034-06Research on Robot Constant Force Grinding and Polishing Control Based on Fuzzy PIDWANG Hong-ping ，XIONG Meng-qiang ，MA Guo-qing（School of Mechanical Engineering ，Changchun University of Science and Technology ，Changchun 130022）Abstract ：In order to improve the grinding and polishing quality of the workpiece surface ，a constant force control method based on fuzzy PID is proposed in this paper.The grinding pressure error e and pressure error change rate ec are used as input variables.The fuzzy adaptive tuning PID method is used to achieve constant force grinding of the robot.In this paper ，this method is compared with the traditional PID method ，and the constant surface grinding based on the FANUC robot is used to perform the actual plane grinding experiment.The results show that the method has faster adjustment speed ，smaller overshoot ，and good stability.The surface roughness of the workpiece with constant force grinding and polishing is between 0.18-0.22μm ，which effectively improves the quality of grinding and polishing.Key words ：grinding and polishing quality ；fuzzy PID ；constant force control ；surface roughness近年来，随着现代制造技术的发展，人们对各种产品的外观需求越来越高，如：卫浴产品、生活电器外壳、工艺品等，传统手工打磨加工的产品表面质量稳定性无法保证。
聚合反应釜温度控制系统设计
聚合反应釜温度控制系统设计摘要聚合反应机理复杂，是强放热反应，过程具有大滞后、大惯性、非线性等特性.温度、压力、浓度及催化剂的活性与牌号等都对化学平衡产生重要影响。
因此，反应釜温度控制的效果将直接影响产品的质量及装置的正常运行，为此将反应釜温度控制回路列为重点监控回路，严格将反应釜温度控制在要求范围内。
传统的PID控制是一种基于过程参数的控制方法，具有控制原理简单、稳定性好、可靠性高、参数易调整等优点,但其设计依赖于被控对象的精确数学模型，在线整定参数的能力差,因反应釜机理复杂，各个参数在系统反应过程中时变。
因而采用一般的PID控制器无法实现对反应釜的精确控制.模糊控制和预测控制都是对不确定系统进行控制的有效方法.本文将模糊控制和预测控制结合起来运用于聚合反应釜温度控制器的设计，设计以聚合反应釜温度控制系统为中心，从控制系统的硬件系统组成、软件选用到系统的设计。
单片机以其功能强、体积小、可靠性高、造价低和开发周期短等优点,成为自动化和各个测控领域中广泛应用的器件，在温度控制系统中，单片机更是起到了不可替代的核心作用。
在工业生产中,如用于热处理的加热炉、用于融化金属的坩锅电阻炉等，都用到了电阻加热的原理.鉴于单片机技术应用的广泛性和优越性，温度控制的重要性，因而设计一种较为理想的温度控制系统是非常有价值的。
本文就是根据这一思想来展开的.结果表明预测模糊控制作为模糊控制和预测控制相结合的产物该控制方法具有使系统超调量小、调整时间短、对系统参数变化和外界干扰有较强的鲁棒性等优点，是一种提高聚合反应釜温度控制效果的有效方法。
关键词：聚合反应；预测控制；模糊控制；单片机Summary of polymerization Kettle temperature controlsystem designABSTRACTPolymerization reaction mechanism for complex，is a strong exothermic reaction, process with large time delays, large inertia, nonlinear and other features。
学习报告格式
学习报告格式篇一：学习报告格式及要求垣曲中学学生撰写学习报告有关要求为了促进学生进一步研读学习知识或总结学习方法与经验教训，提升研究和学习的能力，我校要求学生根据不同学习阶段的任务与要求，撰写学习报告，具体要求如下：一、章节（单元、模块）学习报告1、撰写组织：各科每一张章节、单元或模块学习结束，由各学科组长组织，各教师安排课内时间，统一要求完成时间，并收回批阅。
2、学习报告的内容：（1）所学章节或者模块内容概要（2）所学章节内容中让你深刻体会的部分；（3）本章节或模块中自己用到的最有效的学习方法；（4）本章节或模块中自己学习过程中存在的问题或未解决的学习知（5）学后感想或体会：所学内容引起的疑问、所学内容令你有何提醒、启发及反思、所学内容令你引发的期望等。
（6）从本章节或模块中有何收获；（7）总结3、学习报告的撰写步骤：（1）确立论题：每人根据学习内容感受，自由选取一个自己最感兴趣的角度确立一个论题；选择的角度要小，挖掘要深；（2）撰写报告：态度端正、书写认真、要点清楚（3）报告的内容：选题理由、确立观点、论述观点（4）注意点：语言的流畅、观点与论述的一致。
4、写作要求：总体要求：科学性、创造性、准确性、通俗性具体要求有：（1）所学内容概括要充分。
（2）分析整理要科学。
（3）观点材料要统一。
（4）语言使用要规范。
（5）感想、收获、总结要简练。
二、考试反思报告格式及要求考试反思，就是把自己考试后的得失，进行一次全面系统的评价和具体的分析，分析取得了哪些成绩，存在哪些缺点和不足，有什么经验和提高，写成书面的诉述。
1、考试反思的内容：（1）概述和叙述，有的比较简单，有的比较详细。
这部分内容主要是对自己本次考试后的班级名次，学科名次或考试成绩作一个简洁的概述。
（2）成绩和缺点。
这是反思的中心。
反思的目的就是要肯定成绩，找出缺点。
成绩有哪些，有多大，表现在哪些方面，是怎样取得的；缺点有多少，表现在哪些方面，是什么性质的，怎样产生的，都应讲清楚。
两轮自平衡机器人平衡控制仿真与研究
两轮自平衡机器人平衡控制仿真与研究李世光;王文文;申梦茜;高正中【摘要】To solve the balance instability problem of the two-wheeled self-balance robot,this paper established a dynamics mathematical model of robot and designed a controller based on variable universe fuzzy-PID.With micro-controller ARM STM32F103 as its core,a hardware platform was built and the principle and method of controlling parameters for two-wheeled self-balance robot were described in detail to achieve the balance control of the two-wheeled self-balance robot system.The simulation results show that with faster response speed,higher regulating precision and strong anti-interference ability,the two-wheeled self-balance robot based on variable universe fuzzy-PID can improve the static and dynamic performance and robustness of the system.%针对两轮自平衡机器人的平衡不稳定问题,建立了机器人动力学数学模型,设计了一种基于变论域的模糊PID控制器,以ARM的微控制器STM32F103为核心,搭建硬件平台,详细阐述两轮自平衡机器人控制参数整定的原理和方法,实现了两轮自平衡机器人系统的平衡控制.仿真结果表明:基于变论域模糊PID控制的两轮自平衡机器人响应速度快、抗干扰能力强,能够更好的减小超调量,提高系统的动静态特性和鲁棒性.【期刊名称】《山东科技大学学报（自然科学版）》【年(卷),期】2016(035)006【总页数】6页(P76-81)【关键词】两轮自平衡机器人;数学建模;变论域;模糊PID【作者】李世光;王文文;申梦茜;高正中【作者单位】山东科技大学电气与自动化工程学院,山东青岛 266590;山东科技大学电气与自动化工程学院,山东青岛 266590;山东科技大学电气与自动化工程学院,山东青岛 266590;山东科技大学电气与自动化工程学院,山东青岛 266590【正文语种】中文【中图分类】TP242两轮自平衡机器人是移动机器人研究中的一个重要领域，因其运动灵活、适应地形变化能力强等特点，可胜任一些复杂环境的工作。