PID控制器参数智能整定方法研究
|
摘要
PID控制器是最早发展起来的控制策略之一,因为结构简单,容易实现,并且具有较强的鲁棒性,因而被广泛应用于各种工业过程控制中。控制器的性能直接关系到生产过程的平稳高效运行以及产品的最终质量,因此控制系统的设计主要体现在控制器参数的整定上。随着计算机技术的飞跃发展和人工智能技术渗透到自动控制领域,各种先进PID控制器参数整定方法层出不穷,给PID控制器参数整定的研究带来了无限活力和契机。自整定技术的发展一方面减轻了控制工程师现场调试的工作量,节省了大量的时间,另一方面也使整定的结果更加理想,并使一些复杂但是更加精细的设计方法得以应用于实际工业过程。然而很多先进的PID参数整定方法并没有像预期的那样产生完美的控制效果。所以研究PID控制器参数自整定具有很高的学术和工程应用价值,本文就对这个问题进行一些较为深入的研究。
论文第一章讨论了PID参数整定的意义和发展状况,并介绍了数字PID的几种改进型。第二章详细介绍了遗传算法,利用遗传算法具有不用求导数,不必对问题局部线性化,对初始模型要求较低和鲁棒性强等优点,进行PID参数整定,仿真表明自适应遗传算法具有较强的寻优能力。第三章介绍了DNA遗传算法的基本原理,利用DNA遗传算法十分灵活,DNA染色体长度的可变性,使插入和删除碱基序列的操作更易实现等特点,应用于PID参数整定,仿真表明,采用DNA遗传算法在进化代数相同时能找到比常规遗传算法更优的控制参数,该算法对PID控制参数寻优是实用的和有效的,具有很好的应用前景。第四章第二节在传统的混沌算法的基础上,引入微粒群算法的寻优思想,形成了一种混沌微粒群算法,并应用在PID控制器的参数优化上,仿真证明了该算法能有效地实现PID参数最优整定,寻优速度快,容易实现,为解决PID控制器参数全局最优设计提供了一种新的有效方法。第四章第三节将遗传算法和混沌优化方法智能集成,利用混沌序列的“遍历性、随机性、规律性”的特点生成初始种群,在遗传操作中加入混沌细搜索,大大提高了局部搜索能力,能有效防止遗传算法陷入局部最优和发生早熟现象,仿真表明,混沌遗传算法优化结果相当理想,效果令人满意,优于常规的遗传算法。第五章是对论文的综述和个人的一点展望。
PID is one of the earliest control measures, it is used widely in kinds of industry circumstance for its simple structure, easy implementation and strong robustness. The capability of controller directly influences the qualities of producing process and products. Therefore, parameter tuning of controllers is the most important step during system design. With the development of computer technology and artificial intelligence in automatic control field, all kinds of parameters tuning methods of PID controller have emerged in endlessly, which bring much energy for the study of PID controller. The development of auto-tune release control engineers from field configuration, and save a large amount of time. On the other hand, it makes tuning result more reliable, and some refined but complex methods can be used in practical industrial process control. But many advanced tuning methods behave not so perfect as to be expected as to be expected. So there are academic value and engineering application value which study PID parameter auto-tuning. The paper go deep into studying the question.
In the chapter one, We discusses the meanings of PID parameter auto-tuning methods and researching achievements on this subject. In the chapter two, genetic algorithm was described in detail. GA has such good qualities as no differential coefficient requiring, no local linearization, low request for initial model, robust and so on. It is applied in PID para--meter auto-tuning, simulation results show that the adaptive genetic algorithm has perfect optimization effect. In the chapter three, the basic principles of DNA-GA is presented. DNA-GA is very agile, the length of the DNA chromosome is veried which makes insert and delete DNA sequence easy to realize. It is applied in PID parameter auto- tuning, simulation results show that DNA-GA can search more excellent parameters than GA in the same evolution and that the algorithm for optimizing parameter is applied and effective, and is much better than that of common Genetic Algorithm, and has good perfectible application future. In section two of the chapter four, a new CPSO algorithm is formed by combining the traditional chaos algorithm and Particle Swarm Optimization, and used in PID controller to optimize parameters. Simulation results show that the algorithm is efficient to realize self-tuning global optimal parameters of PID controller, which have the advantages of stability and small overshoot, and it is easy to realize, highly effective and speediness. The algorithm supports a effective method for searching for global optimal PID parameters. In section three of the chapter four, we integrate GA and chaos optimizing, by the use of the chaos serial's property of "ergodicity, randomicity, regularity", original population is generated; adding chaos operator to simple genetic algorithm greatly improves the local search ability, which avoids local optimization and premature convergence in effect. The results of the examples demonstrate that the chaos genetic algorithm has ideal and satisfied optimization result and much better than that of common Genetic Algorithm. The summary of the paper and personal perspective are given in the chapter five.
论文第一章讨论了PID参数整定的意义和发展状况,并介绍了数字PID的几种改进型。第二章详细介绍了遗传算法,利用遗传算法具有不用求导数,不必对问题局部线性化,对初始模型要求较低和鲁棒性强等优点,进行PID参数整定,仿真表明自适应遗传算法具有较强的寻优能力。第三章介绍了DNA遗传算法的基本原理,利用DNA遗传算法十分灵活,DNA染色体长度的可变性,使插入和删除碱基序列的操作更易实现等特点,应用于PID参数整定,仿真表明,采用DNA遗传算法在进化代数相同时能找到比常规遗传算法更优的控制参数,该算法对PID控制参数寻优是实用的和有效的,具有很好的应用前景。第四章第二节在传统的混沌算法的基础上,引入微粒群算法的寻优思想,形成了一种混沌微粒群算法,并应用在PID控制器的参数优化上,仿真证明了该算法能有效地实现PID参数最优整定,寻优速度快,容易实现,为解决PID控制器参数全局最优设计提供了一种新的有效方法。第四章第三节将遗传算法和混沌优化方法智能集成,利用混沌序列的“遍历性、随机性、规律性”的特点生成初始种群,在遗传操作中加入混沌细搜索,大大提高了局部搜索能力,能有效防止遗传算法陷入局部最优和发生早熟现象,仿真表明,混沌遗传算法优化结果相当理想,效果令人满意,优于常规的遗传算法。第五章是对论文的综述和个人的一点展望。
PID is one of the earliest control measures, it is used widely in kinds of industry circumstance for its simple structure, easy implementation and strong robustness. The capability of controller directly influences the qualities of producing process and products. Therefore, parameter tuning of controllers is the most important step during system design. With the development of computer technology and artificial intelligence in automatic control field, all kinds of parameters tuning methods of PID controller have emerged in endlessly, which bring much energy for the study of PID controller. The development of auto-tune release control engineers from field configuration, and save a large amount of time. On the other hand, it makes tuning result more reliable, and some refined but complex methods can be used in practical industrial process control. But many advanced tuning methods behave not so perfect as to be expected as to be expected. So there are academic value and engineering application value which study PID parameter auto-tuning. The paper go deep into studying the question.
In the chapter one, We discusses the meanings of PID parameter auto-tuning methods and researching achievements on this subject. In the chapter two, genetic algorithm was described in detail. GA has such good qualities as no differential coefficient requiring, no local linearization, low request for initial model, robust and so on. It is applied in PID para--meter auto-tuning, simulation results show that the adaptive genetic algorithm has perfect optimization effect. In the chapter three, the basic principles of DNA-GA is presented. DNA-GA is very agile, the length of the DNA chromosome is veried which makes insert and delete DNA sequence easy to realize. It is applied in PID parameter auto- tuning, simulation results show that DNA-GA can search more excellent parameters than GA in the same evolution and that the algorithm for optimizing parameter is applied and effective, and is much better than that of common Genetic Algorithm, and has good perfectible application future. In section two of the chapter four, a new CPSO algorithm is formed by combining the traditional chaos algorithm and Particle Swarm Optimization, and used in PID controller to optimize parameters. Simulation results show that the algorithm is efficient to realize self-tuning global optimal parameters of PID controller, which have the advantages of stability and small overshoot, and it is easy to realize, highly effective and speediness. The algorithm supports a effective method for searching for global optimal PID parameters. In section three of the chapter four, we integrate GA and chaos optimizing, by the use of the chaos serial's property of "ergodicity, randomicity, regularity", original population is generated; adding chaos operator to simple genetic algorithm greatly improves the local search ability, which avoids local optimization and premature convergence in effect. The results of the examples demonstrate that the chaos genetic algorithm has ideal and satisfied optimization result and much better than that of common Genetic Algorithm. The summary of the paper and personal perspective are given in the chapter five.
引文
[1]#12
[2]Astrom K J and Hagglund T.PID controllers,2nd Edition.Research Triangle Park,North Carolina:Instrument Society of America,1995
[3]Hang C.C.,HoWK.,Cao L.S.A comparison of two design methods for PID controllers.ISA TRANS,1994,33(2).
[4]Ziegler J G,Nichols N B.Optimum settings for automatic controllers.Trans.ASME,1942,64:759-768
[5]Astrom K J.Toward intelligent control.IEEE Control Systems Magazine.1989(April):60-64
[6]Astrom K J.Hang C C,Persson P,Ho W K.Towards intelligent PID control.Automatica,1992,28(1):1-9
[7]Qiao W Z.and Mizumoto M.PID Type Fuzzy Controller and Parameters Adaptive Method.Fuzzy Sets and Systems,1996,78(1):23-36
[8]郎文辉.朱尚明,骆德汉.基于虚拟模糊集的PID参数整定器的仿真研究[J].计算机仿真,2000,17(1):28-29
[9]王卫红,张井岗,刘晓星.基于模糊逻辑的二自由度PID调节器参数整定[J].系统工程与电子技术,2003,25(7),845-847
[10]胡锦晖,胡大斌.PID参数模糊自整定控制器的设计与仿真研究[J].海军工程大学学报,2005.17(1):97-100
[11]王耀南,童调生.神经网络智能PID参数最优控制及应用[J].湖南大学学报,1994,21(2):76-79
[12]龚菲,王永骥.基于神经网络的PID参数自整定与实时控制[J].华中科技大学学报(自然科学版),2002,(6):69-71
[13]王俊国,王永骥,万淑芸.基于动态神经网络的PID参数整定与实时控制[J].系统工程与电子技术,2004,26(6):777-778
[14]李萍,赵虎,孟丽霞.神经网络自学习PID控制器[J].仪器仪表学报.2005,26(8)(增刊):511-512
[15]牛建军,吴伟,陈国定.基于神经网络自整定PID控制策略及其仿真[J].系统仿真学报, 2005,17(6):1425-1427
[16]廖芳芳,肖建.基于BP神经网络PID参数自整定的研究[J].系统仿真学报,2005,17(7):1711-1713
[17]李敏强,寇纪淞,林丹等.遗传算法的基本理论与应用[M],北京:科学出版社,2002.
[18]郝晓弘,范波.遗传算法PID参数优化的改进[J].电气传动自动化.2000,20(4):30-32
[19]黄友锐.基于遗传神经网络的自整定PID控制器[J].系统仿真学报..2003,15(11):1628-1631
[20]侯志祥,申群太,李河清.基于改进遗传算法的P1D参数整定及其在加热炉中的应用[J].计算机工程,2004,30(6):165-167
[21]孟安波,叶鲁卿,殷豪等.遗传算法在水电机组调速器PID参数优化中的应用[J].控制理论与应用,2004,21(3):398-404
[22]谭冠政,李文斌.基于蚁群算法的智能人工腿最优PID控制器设计[J].中南大学学报(自然科学版),2004,35(1):91-96
[23]汪新星,张明.利用改进微粒群算法优化PID参数[J].自动化仪表,2004,25(2):19-22
[24]李祥飞,邹恩,,张泰山等.基于混沌优化的规范化PID控制器及其应用[J].中南工业大学学报,2002,33(3):301-304
[25]高金源编著.计算机控制系统-理论、设计与实现[M].北京:北京航空航天大学出版社,112-119
[26]孙增圻编著.智能控制理论与技术[M].北京:清华大学出版社,1997
[27]李敏强,寇纪淞,林丹等.遗传算法的基本理论与应用[M].科学出版社.2002.
[28]Holland,J,H.Adaptation in natural and artificial systems:An introductory analysis with applications to biology,control and artificial intelligence.1~(st) edition.,Ann Arbor,MI:The University of Michigan Press,1975;2nd edition,Cambridge,MA:MIT Press,1992
[29]Goldberg,D.E.Genetic algorithms in search,optimization and machine learning.MA:Addison-Wesley Publishing Company,1989
[30]De Jong,K.A.An analysis of the behavior of a class of genetic adaptive systems(Ph.D Dissertation).University of Michigan,No.76-9381,1975
[31]丁永生.计算智能[M].科学出版社,2004:294-299
[32]Man K F,Tang K S,Kwong S and Halang W A.Genetic algorithms for control and signal processing.Springer,1997
[33]丁永生,任立红,邵世煌.采用DNA编码方法表达的模糊控制规则.99中国控制与决策年会.南京,1999:384-387
[34]Ren L H and Ding Y S.Design of fuzzy control system by a new DNA-based immune genetic algorithm.The 10~(th) IEEE Int.Conf Fuzzy Systems.Melbourne.2001:244-247
[35]Deaton R,Garzon M,Rose J,Murphy R C,Stevens S E.Jr.and Franceschetti D R.A DNA based artificial immune system for self-nonself discrimination.Proc.1997 IEEE int.Conf,on Systems,Man and Cybernetics.Orlando.1997:862-866
[36]孙增圻编著.智能控制理论与技术[M].北京:清华大学出版社,1997
[37]Wasiewica P,Janczak T,Mulawka I J and Plucienniczak A.The inference based on molecular computing.Int.J.Cybernetics and Systems,2000,31(3):283-315
[38]Manganaro G and Gyvez J P D.DNA computing based on chaos,Proc.1997 IEEE Int.Conf.on Evolutionary Computation.Indianapolis.1997:255-260
[39]王孙安,郭子龙.混沌免疫优化组合算法[J].控制与决策,2006,21(2):205-209
[40]柳贺,黄道.混沌优化在模糊系统优化设计中的应用[J].华东理工大学学报,2002,21(2):27-31
[4l]修春波,张宇河.蚁群混沌混合优化算法[J].计算机工程与应用,2006,21(2):43-46
[42]唐巍,张学义,李殿璞.神经网络权值的混沌优化方法研究[J].哈尔滨工程大学学报,2000,21(3):12-14
[43]郭兴众,马健.一类二层多目标规划的混沌遗传优化算法及其应用[J].北京科技大学学报,2006,28(7):696-699
[44]Grassberger P and Procaccia I.Measuring the strangeness of strang atractors,Physica D,1983,9:18
[45]Farmer J D and SWorowich J J.Predicting chaotic time series,Phys.Rev.Lett.,1981,59(8):845-848
[46]Liu Z,Ren X,Zhu Z.Equivalence between differenct local prediction methods of chaotic time series,Phys.Lett.A,1997,22 7:37-40
[47]liguniY,K awamotol,A dachiN.A nonlinear adaptive estimation method based on local approx-imation,IEEE Trans.Signal Processing,1997,45(7):1831-1841
[48]Cuomo K M and Oppenheim A V.Circuit implementation of Synchronized Chaos with Applications to Communication,Phys.Rev.Lett.,1993,71(1):65-68
[49]Feldmann U,Hasler M,Schwartz W.Communication by chaotic signal:the inverse system approach,Int.J.Circ.Theor.Appl.,1996,24:551-579
[50]Baptisa M S.Cryptography with chaos.Phys.Lett.A,1998,240:50-54
[51]王凌,郑大钟,李清生.混沌优化方法的研究进展.计算技术与自动化,2001,20(1):1-5
[52]Kennedy J,Eberhart R C.Particle Swarm Optimization.Proceedings of IEEE International Conference on Neutral Networks,Perth,Australia,1995.
[53]谢晓锋,张文俊,杨之廉.微粒群算法综述[J].控制与决策,2003,18(2):129-134
[54]刘金琨著.先进PID控制及其MATLAB仿真[M].北京:电子工业出版社,2003.
[55]魏光华.基于混沌遗传算法的无速度传感器DTC系统的参数辩识[D].沈阳:沈阳工业大学,2005.
[2]Astrom K J and Hagglund T.PID controllers,2nd Edition.Research Triangle Park,North Carolina:Instrument Society of America,1995
[3]Hang C.C.,HoWK.,Cao L.S.A comparison of two design methods for PID controllers.ISA TRANS,1994,33(2).
[4]Ziegler J G,Nichols N B.Optimum settings for automatic controllers.Trans.ASME,1942,64:759-768
[5]Astrom K J.Toward intelligent control.IEEE Control Systems Magazine.1989(April):60-64
[6]Astrom K J.Hang C C,Persson P,Ho W K.Towards intelligent PID control.Automatica,1992,28(1):1-9
[7]Qiao W Z.and Mizumoto M.PID Type Fuzzy Controller and Parameters Adaptive Method.Fuzzy Sets and Systems,1996,78(1):23-36
[8]郎文辉.朱尚明,骆德汉.基于虚拟模糊集的PID参数整定器的仿真研究[J].计算机仿真,2000,17(1):28-29
[9]王卫红,张井岗,刘晓星.基于模糊逻辑的二自由度PID调节器参数整定[J].系统工程与电子技术,2003,25(7),845-847
[10]胡锦晖,胡大斌.PID参数模糊自整定控制器的设计与仿真研究[J].海军工程大学学报,2005.17(1):97-100
[11]王耀南,童调生.神经网络智能PID参数最优控制及应用[J].湖南大学学报,1994,21(2):76-79
[12]龚菲,王永骥.基于神经网络的PID参数自整定与实时控制[J].华中科技大学学报(自然科学版),2002,(6):69-71
[13]王俊国,王永骥,万淑芸.基于动态神经网络的PID参数整定与实时控制[J].系统工程与电子技术,2004,26(6):777-778
[14]李萍,赵虎,孟丽霞.神经网络自学习PID控制器[J].仪器仪表学报.2005,26(8)(增刊):511-512
[15]牛建军,吴伟,陈国定.基于神经网络自整定PID控制策略及其仿真[J].系统仿真学报, 2005,17(6):1425-1427
[16]廖芳芳,肖建.基于BP神经网络PID参数自整定的研究[J].系统仿真学报,2005,17(7):1711-1713
[17]李敏强,寇纪淞,林丹等.遗传算法的基本理论与应用[M],北京:科学出版社,2002.
[18]郝晓弘,范波.遗传算法PID参数优化的改进[J].电气传动自动化.2000,20(4):30-32
[19]黄友锐.基于遗传神经网络的自整定PID控制器[J].系统仿真学报..2003,15(11):1628-1631
[20]侯志祥,申群太,李河清.基于改进遗传算法的P1D参数整定及其在加热炉中的应用[J].计算机工程,2004,30(6):165-167
[21]孟安波,叶鲁卿,殷豪等.遗传算法在水电机组调速器PID参数优化中的应用[J].控制理论与应用,2004,21(3):398-404
[22]谭冠政,李文斌.基于蚁群算法的智能人工腿最优PID控制器设计[J].中南大学学报(自然科学版),2004,35(1):91-96
[23]汪新星,张明.利用改进微粒群算法优化PID参数[J].自动化仪表,2004,25(2):19-22
[24]李祥飞,邹恩,,张泰山等.基于混沌优化的规范化PID控制器及其应用[J].中南工业大学学报,2002,33(3):301-304
[25]高金源编著.计算机控制系统-理论、设计与实现[M].北京:北京航空航天大学出版社,112-119
[26]孙增圻编著.智能控制理论与技术[M].北京:清华大学出版社,1997
[27]李敏强,寇纪淞,林丹等.遗传算法的基本理论与应用[M].科学出版社.2002.
[28]Holland,J,H.Adaptation in natural and artificial systems:An introductory analysis with applications to biology,control and artificial intelligence.1~(st) edition.,Ann Arbor,MI:The University of Michigan Press,1975;2nd edition,Cambridge,MA:MIT Press,1992
[29]Goldberg,D.E.Genetic algorithms in search,optimization and machine learning.MA:Addison-Wesley Publishing Company,1989
[30]De Jong,K.A.An analysis of the behavior of a class of genetic adaptive systems(Ph.D Dissertation).University of Michigan,No.76-9381,1975
[31]丁永生.计算智能[M].科学出版社,2004:294-299
[32]Man K F,Tang K S,Kwong S and Halang W A.Genetic algorithms for control and signal processing.Springer,1997
[33]丁永生,任立红,邵世煌.采用DNA编码方法表达的模糊控制规则.99中国控制与决策年会.南京,1999:384-387
[34]Ren L H and Ding Y S.Design of fuzzy control system by a new DNA-based immune genetic algorithm.The 10~(th) IEEE Int.Conf Fuzzy Systems.Melbourne.2001:244-247
[35]Deaton R,Garzon M,Rose J,Murphy R C,Stevens S E.Jr.and Franceschetti D R.A DNA based artificial immune system for self-nonself discrimination.Proc.1997 IEEE int.Conf,on Systems,Man and Cybernetics.Orlando.1997:862-866
[36]孙增圻编著.智能控制理论与技术[M].北京:清华大学出版社,1997
[37]Wasiewica P,Janczak T,Mulawka I J and Plucienniczak A.The inference based on molecular computing.Int.J.Cybernetics and Systems,2000,31(3):283-315
[38]Manganaro G and Gyvez J P D.DNA computing based on chaos,Proc.1997 IEEE Int.Conf.on Evolutionary Computation.Indianapolis.1997:255-260
[39]王孙安,郭子龙.混沌免疫优化组合算法[J].控制与决策,2006,21(2):205-209
[40]柳贺,黄道.混沌优化在模糊系统优化设计中的应用[J].华东理工大学学报,2002,21(2):27-31
[4l]修春波,张宇河.蚁群混沌混合优化算法[J].计算机工程与应用,2006,21(2):43-46
[42]唐巍,张学义,李殿璞.神经网络权值的混沌优化方法研究[J].哈尔滨工程大学学报,2000,21(3):12-14
[43]郭兴众,马健.一类二层多目标规划的混沌遗传优化算法及其应用[J].北京科技大学学报,2006,28(7):696-699
[44]Grassberger P and Procaccia I.Measuring the strangeness of strang atractors,Physica D,1983,9:18
[45]Farmer J D and SWorowich J J.Predicting chaotic time series,Phys.Rev.Lett.,1981,59(8):845-848
[46]Liu Z,Ren X,Zhu Z.Equivalence between differenct local prediction methods of chaotic time series,Phys.Lett.A,1997,22 7:37-40
[47]liguniY,K awamotol,A dachiN.A nonlinear adaptive estimation method based on local approx-imation,IEEE Trans.Signal Processing,1997,45(7):1831-1841
[48]Cuomo K M and Oppenheim A V.Circuit implementation of Synchronized Chaos with Applications to Communication,Phys.Rev.Lett.,1993,71(1):65-68
[49]Feldmann U,Hasler M,Schwartz W.Communication by chaotic signal:the inverse system approach,Int.J.Circ.Theor.Appl.,1996,24:551-579
[50]Baptisa M S.Cryptography with chaos.Phys.Lett.A,1998,240:50-54
[51]王凌,郑大钟,李清生.混沌优化方法的研究进展.计算技术与自动化,2001,20(1):1-5
[52]Kennedy J,Eberhart R C.Particle Swarm Optimization.Proceedings of IEEE International Conference on Neutral Networks,Perth,Australia,1995.
[53]谢晓锋,张文俊,杨之廉.微粒群算法综述[J].控制与决策,2003,18(2):129-134
[54]刘金琨著.先进PID控制及其MATLAB仿真[M].北京:电子工业出版社,2003.
[55]魏光华.基于混沌遗传算法的无速度传感器DTC系统的参数辩识[D].沈阳:沈阳工业大学,2005.