大时滞系统参数自整定控制的研究
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摘要
工业生产过程中,时滞对象普遍存在,同时也是较难控制的,尤其是大时滞对象的控制一直都是一个难题。而很多温度控制系统都是属于大时滞系统,常见的智能温度控制器虽然在温度控制的实际应用中表现了比较理想的控制效果,但它仍然属于将参数整定与系统控制分开处理的离线整定方法,如果工况发生变化就必须重新调整参数。针对这一问题,为了实现时滞系统参数自整定的控制,本文将神经网路控制、模糊控制和PID控制结合起来,设计了基于神经网路的模糊自适应PID控制器。
首先,本论文分析了时滞系统的特点,讨论了几种时滞系统较为成熟的常规控制算法:微分先行控制算法、史密斯预估控制算法、大林控制算法,并深入研究了它们的控制性能;并且通过仿真对这三种控制方法在温控系统中的控制性能进行了比较。
其次,在分析PID参数自整定传统方法的基础上,设计了一种改进方法,并设计了相应的控制器。该控制器综合了模糊控制、神经网络控制和PID控制各自的长处,既具备了模糊控制简单有效的控制作用以及较强的逻辑推理功能,也具备了神经网络的自适应、自学习的能力,同时也具备了传统PID控制的广泛适应性。该方法不需要离线整定参数,实现了在线自整定参数。仿真实验表明了该控制器对模型和环境都具有较好的适应能力和较强的鲁棒性。
最后将基于神经网路的模糊自适应PID控制器应用于贝加莱PID温控装置,能够出色地实现参数的在线自整定。理论分析、系统仿真、实验结果都证实了这种控制策略能有效地减少系统超调量,并减少了调节时间,提高了系统的实时性和控制精度。
Time-delay systems are very widely used in industry and are hard to control, especially when the plant with a long delay. However, a lot of temperature control systems belong to time-delay systems. Although the classical intelligent temperature controller in the practical application of temperature control shows ideal control effect, it still adopts offline tuning methods to solve parameter setting and to control system separately. If the situation changes the parameter must be readjusted. Aimed at the problem, by combining of neural network control, fuzzy control, and PID control, a fuzzy adaptive PID controller based on neural network is designed to adjust parameters online to achieve high performance.
Firstly, this thesis analyzes the characteristics of time-delay system, discusses some general control schemes for time-delay, for example, differential forward control algorithm, Smith predictor control algorithm, Dahlin control algorithm. And it analyzes their control performance. And the result of simulation shows the difference of their performance in temperature control systems.
Secondly, the thesis describes the traditional principle of PID parameter control, and designes a improved scheme and its relevant controller. This controller comprehensively combines the advantage of the neural network control, fuzzy control and PID control. It possesses not only the simplicity control and strong logical inference of the fuzzy control, but also the learning and adaptive functions by using the neural network. Furthermore, it is as widely adaptive as PID controller. It realizes the PID parameters' tuning online and don't need offline tuning. It has proven that this controller has adaptability and robustness to the environment.
Finally, the thesis adopts the fuzzy adaptive PID controller based on neural network to the B&R temperature control system. That could finish the PID parameters' tuning online and obtain perfect control effect. Theoretical analysis, simulation and experiment results have proven that the strategy could decrease the overshoot and shorten the tuning time, improve the system's real-time performance and precision.
首先,本论文分析了时滞系统的特点,讨论了几种时滞系统较为成熟的常规控制算法:微分先行控制算法、史密斯预估控制算法、大林控制算法,并深入研究了它们的控制性能;并且通过仿真对这三种控制方法在温控系统中的控制性能进行了比较。
其次,在分析PID参数自整定传统方法的基础上,设计了一种改进方法,并设计了相应的控制器。该控制器综合了模糊控制、神经网络控制和PID控制各自的长处,既具备了模糊控制简单有效的控制作用以及较强的逻辑推理功能,也具备了神经网络的自适应、自学习的能力,同时也具备了传统PID控制的广泛适应性。该方法不需要离线整定参数,实现了在线自整定参数。仿真实验表明了该控制器对模型和环境都具有较好的适应能力和较强的鲁棒性。
最后将基于神经网路的模糊自适应PID控制器应用于贝加莱PID温控装置,能够出色地实现参数的在线自整定。理论分析、系统仿真、实验结果都证实了这种控制策略能有效地减少系统超调量,并减少了调节时间,提高了系统的实时性和控制精度。
Time-delay systems are very widely used in industry and are hard to control, especially when the plant with a long delay. However, a lot of temperature control systems belong to time-delay systems. Although the classical intelligent temperature controller in the practical application of temperature control shows ideal control effect, it still adopts offline tuning methods to solve parameter setting and to control system separately. If the situation changes the parameter must be readjusted. Aimed at the problem, by combining of neural network control, fuzzy control, and PID control, a fuzzy adaptive PID controller based on neural network is designed to adjust parameters online to achieve high performance.
Firstly, this thesis analyzes the characteristics of time-delay system, discusses some general control schemes for time-delay, for example, differential forward control algorithm, Smith predictor control algorithm, Dahlin control algorithm. And it analyzes their control performance. And the result of simulation shows the difference of their performance in temperature control systems.
Secondly, the thesis describes the traditional principle of PID parameter control, and designes a improved scheme and its relevant controller. This controller comprehensively combines the advantage of the neural network control, fuzzy control and PID control. It possesses not only the simplicity control and strong logical inference of the fuzzy control, but also the learning and adaptive functions by using the neural network. Furthermore, it is as widely adaptive as PID controller. It realizes the PID parameters' tuning online and don't need offline tuning. It has proven that this controller has adaptability and robustness to the environment.
Finally, the thesis adopts the fuzzy adaptive PID controller based on neural network to the B&R temperature control system. That could finish the PID parameters' tuning online and obtain perfect control effect. Theoretical analysis, simulation and experiment results have proven that the strategy could decrease the overshoot and shorten the tuning time, improve the system's real-time performance and precision.
引文
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[29]Berger M..Self-tuning of a PI Controller Using Fuzzy Logic for a Construc- tion Unit Testing Apparatus.Control Eng.Practice,1996,4(6):785-790
[30]Rico J.E.,Bordons C.,Camacho E.F..Improving the Robustness of Deadtime Compensating PI Controllers.Control Eng.Practice,1997,5(6):801-810
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[34]柴天佑,张贵军.基于给定的相角裕度和幅值裕度的PID参数自整定新方法[J].自动化学报,1997,23(2):167-172
[35]汪洋,褚健.一种基于递推参数估计的PID参数自整定方法[J].信息与控制,1996,3:182-185
[36]李卓,萧德云,何世忠.基于神经网络的模糊自适应PID控制方法[J].控制与决策,1996,11(3):340-345
[37]刘曙光,魏俊敏,竺志超.模糊控制技术[M].北京:中国纺织出版社,2001.6:59-102
[38]L.A.Zadeh.Fuzzy Sets[J].Information and Control,1965,8:338-353
[39]E.H.Mandani.Application of Fuzzy Algorithms for the Control of a Dynamic Plant[J].Proc.IEEE 1974,121(12):1585-1588
[40]朱良红,王永初.模糊控制技术在过程控制中的应用研究现状与展望[J].福建电脑.2003,9:8-9
[41]B.A.M.Wakileh and K.F.Gill.Robot Control Using Self-Organizing Fuzzy Logic,Computer in Industry.1990,15:175-186
[42]易继锴,侯媛彬.智能控制技术.北京:北京工业大学出版社,1999.9,177-236
[43]Deng Xiantu.Generating Rules Fuzzy Logic Controllers by Functions.Fuzzy sets and Systems,1990,36(1):83-89
[44]田景文,高美娟.人工神经网络算法研究及应用[M].北京:北京理工大学出版社,2006.7,24-40
[45]邹开其.模糊神经网络的若干进展[J].浙江万里学院学报,2003,16(4): 54-57
[46]陶永华.新型PID控制及其应用[M].北京:机械工业出版社出版,2002,136-184
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[48]董长虹.神经网络与应用[M].北京:国防工业出版社,2005.1,64-120
[49]薛弘晔.计算机控制技术[M].西安:西安电子科技大学出版社,2003,136-137
[50]Smith.O.J.M..Closer Control of Loops with Dead Time[J].Chemical Engineering Progress,1957,53(5):217-219
[51]刘洪波,李少远,贾磊.基于自整定和模糊自校正技术的改进Smith控制系统[J].系统仿真学报,2004,(16):2086-2090
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[2]张峻颖,杨马英,谢启.含有纯滞后系统的控制方法[J].控制工程,2002,9(6):91-93
[3]宋云霞,朱学峰.大时滞过程控制方法及应用[J].化工自动化及仪表,2001,28(4):9-15
[4]郑柯,孙一康.时滞过程控制算法分析[J].北京科技大学学报,2000,22(5):486-488
[5]王正林,郭阳宽.过程控制与Simulink应用[M].北京:电子工业出版社,2006.7:163-176
[6]翁维勤,周庆海.过程控制系统及工程[M].北京:化学工业出版社,2002.9:19-36
[7]Watanabe K.,Nobuyama E.,Kojima A..Recent Advances in Control of Time Delay Systems:A Tutorial Review[A].Proceedings of the 35th Conference on Decision and Control.Kobe,Japan,1996:2083-2089
[8]曹永岩,毛维杰,孙优贤,冯旭.现代控制理论的工程应用.杭州:浙江大学出版社,2000.5:120-145
[9]Futao Zhao,Jing Ou,Wei Du.Pattern-based Fuzzy Predictive Control for A Chemical Process with Dead Time.Engineering Applications of Artificial Intel-ligence.2000,13(1):37-45
[10]Seng Teo Lian,Khalid Marzulci,Yusof Rubiyah.Tuning of A Neuro-fuzzy Controller by Genetic Algorithms with A Application to A Coupled-tank Liquid-level Control System.Engineering Applications of Artificial Intelligence.1998,11(4):517-529
[11]谭永红.基于BP神经网络的自适应控制[J].控制理论与应用,1994,11(1):84-87
[12]Zigerler J.G.,Nichols N.B..Optimum Setting for Automatic Controller[J].Trans ASME.1942,64(8):759-768
[13]Cohen G.H.,Coon G.A..Theoretical consideration of retarded Control[J].Trans ASME,1953,75:827-834
[14]Hang C.C.,Astrom K.J..Ho W.K.Refinement of the Zigerler-Nichols Tuning Formula[J].IEEE.Proceeding,PartD,1991,138(2):111-118
[15]王伟,张晶涛,柴天佑.PID参数先进整定方法综述[J],自动化学报,2000,26(3):347-355
[16]Astrom K.J.,Hagglund T.,Wallenborg A..Automatic Tuning of PID Controllers.Research Triangle Park,North Carolina:Instrument Society of America,1988
[17]Hang C.C.,Astrom K.J.,Ho W.K..Relay Auto-tuning in the Presence of Static Load Disturbance,Automatica,1993,29(2):563-564
[18]Lundh M.,Astrom K.J.,Automatic Initialization of A Robust Self-tuning Controller,Automatica,1994,30(11):1649-1662
[19]Yusof R.,Omatu S.,Khaild M.,Self-tuning PID Control:A Multivariable Derivation and Application,Automatica,1994,30(12):1975-1981
[20]Schei T.S..Automatic Tuning of PID Controllers Based on Transfer Function Estimation,Automatica,1994,30(12):1983-1989
[21]Voda A.A.,Landau L.D.,A Method for the Auto-calibration of PID Controllers,Automatica,1995,31(1):41-53
[22]Lordon L..Tuning of PID Controllers by the Non-symmetrical Optimum Method,Automatics,1997,33(1):103-107
[23]Park J.H.,Sung S.W.,and Lee I.B..Improved Relay Auto-tuning with Static Load Disturbance,Automatic,1997,33(4):711-715
[24]Wang Q.G.,Zou B.,Lee T.H.,and Bi Q..Auto-tuning of Multivariable PID Controllers from Decentralized Relay Feedback,Automatic,1997,33(3):319-330
[25]Ho W.K.,Hang C.C.,and Cao L.S..Tuning of PID Controllers Based on Gain and Phase Margin Specification,Automatica,1995,31:497-502
[26]Ho W.K.,Lim K.W.,Xu W.,Optimal Gain and Phase Marign Tuning for PID Controllers,Automatica,1998,34(8):1009-1014
[27]Astrom K.J.,Hagglund T.,Hang C.C.,Ho W.K..Automatic Tuning and Adaptation for PID Controllers-A Survey,Control Eng.Practice,1993,1(4):699-714
[28]Ho W.K.,et al..Frequency Domain Approach to Self-tuning PID Control.Control Eng.Practice,1996,4(6):807-813
[29]Berger M..Self-tuning of a PI Controller Using Fuzzy Logic for a Construc- tion Unit Testing Apparatus.Control Eng.Practice,1996,4(6):785-790
[30]Rico J.E.,Bordons C.,Camacho E.F..Improving the Robustness of Deadtime Compensating PI Controllers.Control Eng.Practice,1997,5(6):801-810
[31]Hang C.C.,Loh A.P.,Vasnani V.U..Relay feedback auto-tuning of cascade controllers.IEEE Trans.On Control System Tech.,1994,2(1):42-45
[32]Mc Cormack A.S.,Godfrey K.R..Rule-based Auto-tuning Based on Frequency Domain Identification.IEEE Trans.On Control System Tech.,1998,6(1):43-61
[33]陈建勤,陈来九.一种新的PID调节器参数自整定方法[J].控制与决策,1993,8(1):13-18
[34]柴天佑,张贵军.基于给定的相角裕度和幅值裕度的PID参数自整定新方法[J].自动化学报,1997,23(2):167-172
[35]汪洋,褚健.一种基于递推参数估计的PID参数自整定方法[J].信息与控制,1996,3:182-185
[36]李卓,萧德云,何世忠.基于神经网络的模糊自适应PID控制方法[J].控制与决策,1996,11(3):340-345
[37]刘曙光,魏俊敏,竺志超.模糊控制技术[M].北京:中国纺织出版社,2001.6:59-102
[38]L.A.Zadeh.Fuzzy Sets[J].Information and Control,1965,8:338-353
[39]E.H.Mandani.Application of Fuzzy Algorithms for the Control of a Dynamic Plant[J].Proc.IEEE 1974,121(12):1585-1588
[40]朱良红,王永初.模糊控制技术在过程控制中的应用研究现状与展望[J].福建电脑.2003,9:8-9
[41]B.A.M.Wakileh and K.F.Gill.Robot Control Using Self-Organizing Fuzzy Logic,Computer in Industry.1990,15:175-186
[42]易继锴,侯媛彬.智能控制技术.北京:北京工业大学出版社,1999.9,177-236
[43]Deng Xiantu.Generating Rules Fuzzy Logic Controllers by Functions.Fuzzy sets and Systems,1990,36(1):83-89
[44]田景文,高美娟.人工神经网络算法研究及应用[M].北京:北京理工大学出版社,2006.7,24-40
[45]邹开其.模糊神经网络的若干进展[J].浙江万里学院学报,2003,16(4): 54-57
[46]陶永华.新型PID控制及其应用[M].北京:机械工业出版社出版,2002,136-184
[47]刘金琨.先进PID控制MATLAB仿真(第二版)[M].北京:电子工业出版社,2004.9,153-170
[48]董长虹.神经网络与应用[M].北京:国防工业出版社,2005.1,64-120
[49]薛弘晔.计算机控制技术[M].西安:西安电子科技大学出版社,2003,136-137
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