智能电机测试与控制系统的研究与开发
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摘要
随着电机工业发展，进入规模化生产的今天，中小型电机总产量每年以2％的幅度增长，为确保产品的高质量，每台电机出厂前都要进行参数检测，因此，国内研制一套高准确度、高自动化程度的中小电机自动测试系统对提高生产效率、减轻工作强度和提高质量都有重大的现实意义。
在交流伺服系统的控制中，经典的PID控制由于其结构简单、鲁棒性强以及方便现场对其使用和整定，而且它所涉及的控制算法和控制结构非常简单等原因，使它在目前的控制方案中依然是应用最广泛的控制策略之一。但由于电机本身存在非线性问题，如电枢反映的非线性、随负载和工况而变化的转动惯量、电阻变化等难以确定精确的数学模型的问题，使得传统的PID调节器在高精度伺服等场合难以保持良好的性能。
本文通过对智能电机测试与控制系统的设计分析，从现代控制理论方法中总结出来几种适应电机交流伺服系统的控制方法：模糊PID控制器和神经网络PID控制器，并对这2种方法进行仿真实验和实际系统的设计。通过对模糊PID控制器和神经网络PID控制器的仿真研究，得出：将先进的控制理论运用到传统的PID控制器中，其控制性能明显优于传统的PID控制器。
电机测试与控制系统的设计，不仅包括系统硬件的设计，还包括系统软件的设计。在本文中，作者对这两个方面做了比较全面的阐述。提出了一套切实可行的系统设计方案，供读者参考，同时，由于作者的水平有限，论文中可能存在许多不足之处，有待进一步研究。
Developing along with the electrical motor industry, and entering the large scale production today, total yield of small scaled electrical motor every year is then 2% increased, and for insuring the product's high quantity, each pedestal electrical motor outs from factory all want proceeding the automatic examination, therefore, domestic's research to manufacture a high and accurate degree, high automation degree test System to increase the production efficiency, alleviate the work strength and increase the quantity is important with the realistic meaning.
In the servo system of the control, the classic PID control, because of its construction simple, as to it's the strong robust and convenient to apply, and that its calculate way and the construction are very simple, is now still one of the most extensive control strategies which applied. But because the electrical engineering esse is not line problem, reflecting with armature's non-linearity, the inertia varying with load and work condition, the electric resistance variety and other mathematics model that difficult to accurate, the classic PID modulator is difficult to keep high accuracy with the good function in the servo situation.
This paper sum up some kind of control strategies such as Fuzzy PID controller and NN PID controller in servo system from modern control theories method. Through design and simulation on intelligent electrical motor test and control system using this two kinds of methods, this paper summaries that: the PID controller's function using modern control theories is obviously better than tradition of PID controller.
The electrical engineering tests and control system's design, and not only include the design of the system hardware, and still include the design of the system software. In this paper, author does more completely expatiation on these two aspects, and brings out a set system design in practice, providing the reader for reference, at the same time, because of author's level in this field, thesis inside may exist the place of a lot of lack, need the further research.
在交流伺服系统的控制中，经典的PID控制由于其结构简单、鲁棒性强以及方便现场对其使用和整定，而且它所涉及的控制算法和控制结构非常简单等原因，使它在目前的控制方案中依然是应用最广泛的控制策略之一。但由于电机本身存在非线性问题，如电枢反映的非线性、随负载和工况而变化的转动惯量、电阻变化等难以确定精确的数学模型的问题，使得传统的PID调节器在高精度伺服等场合难以保持良好的性能。
本文通过对智能电机测试与控制系统的设计分析，从现代控制理论方法中总结出来几种适应电机交流伺服系统的控制方法：模糊PID控制器和神经网络PID控制器，并对这2种方法进行仿真实验和实际系统的设计。通过对模糊PID控制器和神经网络PID控制器的仿真研究，得出：将先进的控制理论运用到传统的PID控制器中，其控制性能明显优于传统的PID控制器。
电机测试与控制系统的设计，不仅包括系统硬件的设计，还包括系统软件的设计。在本文中，作者对这两个方面做了比较全面的阐述。提出了一套切实可行的系统设计方案，供读者参考，同时，由于作者的水平有限，论文中可能存在许多不足之处，有待进一步研究。
Developing along with the electrical motor industry, and entering the large scale production today, total yield of small scaled electrical motor every year is then 2% increased, and for insuring the product's high quantity, each pedestal electrical motor outs from factory all want proceeding the automatic examination, therefore, domestic's research to manufacture a high and accurate degree, high automation degree test System to increase the production efficiency, alleviate the work strength and increase the quantity is important with the realistic meaning.
In the servo system of the control, the classic PID control, because of its construction simple, as to it's the strong robust and convenient to apply, and that its calculate way and the construction are very simple, is now still one of the most extensive control strategies which applied. But because the electrical engineering esse is not line problem, reflecting with armature's non-linearity, the inertia varying with load and work condition, the electric resistance variety and other mathematics model that difficult to accurate, the classic PID modulator is difficult to keep high accuracy with the good function in the servo situation.
This paper sum up some kind of control strategies such as Fuzzy PID controller and NN PID controller in servo system from modern control theories method. Through design and simulation on intelligent electrical motor test and control system using this two kinds of methods, this paper summaries that: the PID controller's function using modern control theories is obviously better than tradition of PID controller.
The electrical engineering tests and control system's design, and not only include the design of the system hardware, and still include the design of the system software. In this paper, author does more completely expatiation on these two aspects, and brings out a set system design in practice, providing the reader for reference, at the same time, because of author's level in this field, thesis inside may exist the place of a lot of lack, need the further research.
引文
[1]易继锴．候媛彬，智能控制技术．北京：北京大学出版社，1999
[2]李人厚．智能控制理论和方法．西安：西安电子科技大学出版社，2000
[3]韩安荣．通用变频器及其应用．北京：机械工业出版社，2001
[4]陶永华．尹怡欣，葛芦生．新型PID控制器及其应用．北京：机械工业出版社，2001
[5]陶伟民．人工智能技术及应用．上海：同济大学出版社，2000
[6]基于MATLAB的系统分析与设计—神经网络．西安：西安电子科技大学出版社，2001
[7]王树青．先进控制技术及应用．北京：化学工业出版社，1999
[8]胡守仁．神经网络应用技术．长沙：国防科技大学出版社，1999
[9]蔡自兴．智能控制基础与应用．北京：国防工业出版社，1998
[10]Mohand mokhtar，Michel Marie．MATLAB与SIMULINK工程应用．北京：电子工业出版社，2001
[11]常晓生，李伟．不确定性操作臂的混合模糊P+ID控制的实验研究．控制理论与应用，2000,(2)：73—77
[12]何玉彬．神经网络在线学习模糊自适应控制及其应用．中国电机工程学报，2000,(10)：67—70
[13]楚彦君．PID神经网络控制器的设计及仿真．工业仪表与自动化装置，2001,(2)：3—6
[14]王炎．模糊免疫PID控制器的设计与仿真．计算机仿真，2002,(3)：67—69
[15]林瑞全．增益自调整的神经元二自由度PID控制的SIMULINK仿真模型．计算机仿真，2002,(1)：20—22
[16]程启明．改进型PID参数神经网络自学习的船舶操纵控制器．系统仿真学报,2001,(5)：344-346
[17]王耀南．An Adaptive Control Using Fuzzy Logic Neural Network and Its Application．控制理论与应用，1995,(12)：437-447
[18]梁坚等．一种新型的在线实时自调整PID控制器．自动化与仪器仪表,1996,(3)：32-35
[19]李卓等．基于Fuzzy推理的自调整PID控制器．控制理论与应
用，1997,(2)：238-243
[20]纪宗南Fuzzy-PID控制器的自动调节设计．电气自动化，1996(1)：13-15
[21]舒迪前，李春涛，尹怡欣．具有二次型性能指标的单神经元自适应PID控制器及其应用．电气自动化,1997,19(1)：4-7
[22]舒迪前，李春涛，尹怡欣．单神经元自适应PID控制器及其在电加热炉上的应用．电气传动，1995,25(1)：29-32
[23]谭永红．基于BP神经网络的自适应控制．控制理论与应用，1994,11(1)：84-87
[24]李友善，李军等．模糊控制理论及其在过程控制中的应用．北京：国防工业出版社，1993
[25]Mamdani E.H. Development in Fuzzy Logic Control. Pro of 23rd Conference on Decision and Control, 1984
[26]雷勇．单参数PID模糊控制器的设计．电子技术应用，1992(4)：6-10
[27]王耀南．智能控制系统．长沙：湖南大学出版社，1996
[28]King P. J. and Mamdani E. H. The Application of fuzy Control System to Indusrial Processes,6th IFAC Word Congr, Boston,Mass,1975
[29]Yuan-Yih Hsu, Kan-Lee liou. Desing of self-turning PID power system stabilizers forsynchronous generators. IEEE Trans EC,1987,2(3):343-348
[30]Tjokro S, Shah S L. Adaptive PID Control. Proceedings of the 1985 American Control Conterence. 1985:1528-1534
[31]Kong S G and Koska B. Adaptive Fuzzy Systems for Backin up a Truck-and-Trailer. IEEE Trans. Neural Networks,1992,3(2):211-223
[32]Buakata T and Jan Y. Fuzzy Systems:An overview. Commun. ACM, 1994, 37(3):69-76
[33]Zimmermann H J. Fuzzy Set Theory and Its Applications. 2nd rev. ed. Boston: Kluver Acadamic, 1991
[34]Wang L X. Adaptive Fuzzy System and Control, Englewood Cliffs, NT: Prentice-Hall, 1994
[35]Takagi T and Sugeno M. Fuzzy Identification of Systems and Its Application to Modeling and Control. IEEE Transaction on Systems, Man and Cybernetics, 1985, 15: 116—132
[36]Li X F. and Lau C C. Development of Fuzzy Algorhms for Servo Systems. Control System Magazine, 1989, 4: 65—72
[37]Narrendra K S and Parthasarathy K. Identification and Control of Dynamical Systems Using Neural Networks. IEEE Transcations on Neural Networks, 1990, 1(1): 4—27
[38]Fukuda T and Shibata T, Theory and Applications of Neural Networks for Industrial Control Systems. IEEE Trans, on Industrial Electronics, 1992, 39(6):472-489
[39]Levin A U and Narendra K S. Control of Non-linear Dynamical Systems Using neural networks. IEEE Trans. On Neural Network, 1993, 4(2): 182-206
[40]Sartor M A and Antsaklis P J. Imlementations of Learing Control Systems Using Neural Networks. IEEE Control Systems, 1992, 4:31-35
[41]舒迪前等．智能控制系统及其应用．北京：机械工业出版社，1995
[42]杨行峻，郑君里．人工神经网络．北京：高等教育出版社，1992
[43]舒迪前．预测控制系统及其应用．北京：机械工业出版社，1996
[44]Y.Q.Lei, Z.Z.Wang&L.Q.Zhang. Neural Network for Adaptive PID Controller[J].IFA C Intellegence Control Conference 1991
[44]李晓枫，吴惠雁，张海鹰．在分散控制系统上实现的PID参数模糊自整定控制．工业仪表与自动化装置，2000(5)：31—35
[45]MICROMASTER Vector MIDIMASTER Vector 操作手册. SIEMENS,1998
[46]周英玉．混合智能控制器及其工业应用的仿真研究：[硕士学位论文]．大连：大连海事大学，2001
[47]罗瑞琼．包装印刷传动智能控制系统的研究：[硕士学位论文]．长沙：湖南大学，2001
[48]VxWorks 5.4—ds.pdf. Wind River Systems Corporation
[2]李人厚．智能控制理论和方法．西安：西安电子科技大学出版社，2000
[3]韩安荣．通用变频器及其应用．北京：机械工业出版社，2001
[4]陶永华．尹怡欣，葛芦生．新型PID控制器及其应用．北京：机械工业出版社，2001
[5]陶伟民．人工智能技术及应用．上海：同济大学出版社，2000
[6]基于MATLAB的系统分析与设计—神经网络．西安：西安电子科技大学出版社，2001
[7]王树青．先进控制技术及应用．北京：化学工业出版社，1999
[8]胡守仁．神经网络应用技术．长沙：国防科技大学出版社，1999
[9]蔡自兴．智能控制基础与应用．北京：国防工业出版社，1998
[10]Mohand mokhtar，Michel Marie．MATLAB与SIMULINK工程应用．北京：电子工业出版社，2001
[11]常晓生，李伟．不确定性操作臂的混合模糊P+ID控制的实验研究．控制理论与应用，2000,(2)：73—77
[12]何玉彬．神经网络在线学习模糊自适应控制及其应用．中国电机工程学报，2000,(10)：67—70
[13]楚彦君．PID神经网络控制器的设计及仿真．工业仪表与自动化装置，2001,(2)：3—6
[14]王炎．模糊免疫PID控制器的设计与仿真．计算机仿真，2002,(3)：67—69
[15]林瑞全．增益自调整的神经元二自由度PID控制的SIMULINK仿真模型．计算机仿真，2002,(1)：20—22
[16]程启明．改进型PID参数神经网络自学习的船舶操纵控制器．系统仿真学报,2001,(5)：344-346
[17]王耀南．An Adaptive Control Using Fuzzy Logic Neural Network and Its Application．控制理论与应用，1995,(12)：437-447
[18]梁坚等．一种新型的在线实时自调整PID控制器．自动化与仪器仪表,1996,(3)：32-35
[19]李卓等．基于Fuzzy推理的自调整PID控制器．控制理论与应
用，1997,(2)：238-243
[20]纪宗南Fuzzy-PID控制器的自动调节设计．电气自动化，1996(1)：13-15
[21]舒迪前，李春涛，尹怡欣．具有二次型性能指标的单神经元自适应PID控制器及其应用．电气自动化,1997,19(1)：4-7
[22]舒迪前，李春涛，尹怡欣．单神经元自适应PID控制器及其在电加热炉上的应用．电气传动，1995,25(1)：29-32
[23]谭永红．基于BP神经网络的自适应控制．控制理论与应用，1994,11(1)：84-87
[24]李友善，李军等．模糊控制理论及其在过程控制中的应用．北京：国防工业出版社，1993
[25]Mamdani E.H. Development in Fuzzy Logic Control. Pro of 23rd Conference on Decision and Control, 1984
[26]雷勇．单参数PID模糊控制器的设计．电子技术应用，1992(4)：6-10
[27]王耀南．智能控制系统．长沙：湖南大学出版社，1996
[28]King P. J. and Mamdani E. H. The Application of fuzy Control System to Indusrial Processes,6th IFAC Word Congr, Boston,Mass,1975
[29]Yuan-Yih Hsu, Kan-Lee liou. Desing of self-turning PID power system stabilizers forsynchronous generators. IEEE Trans EC,1987,2(3):343-348
[30]Tjokro S, Shah S L. Adaptive PID Control. Proceedings of the 1985 American Control Conterence. 1985:1528-1534
[31]Kong S G and Koska B. Adaptive Fuzzy Systems for Backin up a Truck-and-Trailer. IEEE Trans. Neural Networks,1992,3(2):211-223
[32]Buakata T and Jan Y. Fuzzy Systems:An overview. Commun. ACM, 1994, 37(3):69-76
[33]Zimmermann H J. Fuzzy Set Theory and Its Applications. 2nd rev. ed. Boston: Kluver Acadamic, 1991
[34]Wang L X. Adaptive Fuzzy System and Control, Englewood Cliffs, NT: Prentice-Hall, 1994
[35]Takagi T and Sugeno M. Fuzzy Identification of Systems and Its Application to Modeling and Control. IEEE Transaction on Systems, Man and Cybernetics, 1985, 15: 116—132
[36]Li X F. and Lau C C. Development of Fuzzy Algorhms for Servo Systems. Control System Magazine, 1989, 4: 65—72
[37]Narrendra K S and Parthasarathy K. Identification and Control of Dynamical Systems Using Neural Networks. IEEE Transcations on Neural Networks, 1990, 1(1): 4—27
[38]Fukuda T and Shibata T, Theory and Applications of Neural Networks for Industrial Control Systems. IEEE Trans, on Industrial Electronics, 1992, 39(6):472-489
[39]Levin A U and Narendra K S. Control of Non-linear Dynamical Systems Using neural networks. IEEE Trans. On Neural Network, 1993, 4(2): 182-206
[40]Sartor M A and Antsaklis P J. Imlementations of Learing Control Systems Using Neural Networks. IEEE Control Systems, 1992, 4:31-35
[41]舒迪前等．智能控制系统及其应用．北京：机械工业出版社，1995
[42]杨行峻，郑君里．人工神经网络．北京：高等教育出版社，1992
[43]舒迪前．预测控制系统及其应用．北京：机械工业出版社，1996
[44]Y.Q.Lei, Z.Z.Wang&L.Q.Zhang. Neural Network for Adaptive PID Controller[J].IFA C Intellegence Control Conference 1991
[44]李晓枫，吴惠雁，张海鹰．在分散控制系统上实现的PID参数模糊自整定控制．工业仪表与自动化装置，2000(5)：31—35
[45]MICROMASTER Vector MIDIMASTER Vector 操作手册. SIEMENS,1998
[46]周英玉．混合智能控制器及其工业应用的仿真研究：[硕士学位论文]．大连：大连海事大学，2001
[47]罗瑞琼．包装印刷传动智能控制系统的研究：[硕士学位论文]．长沙：湖南大学，2001
[48]VxWorks 5.4—ds.pdf. Wind River Systems Corporation