主动式磁悬浮轴承转子系统控制方法的研究
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摘要
本文针对主动式磁悬浮轴承转子控制系统，从扰动观点建立了控制系统模型及分析了系统特性，研究了神经网络理论在磁悬浮轴承控制中的应用，并探讨了数字信号处理器(DSP)为核心的控制系统的结构。
首先，本文简要介绍了磁悬浮轴承的工作原理，对电磁力进行了分析和计算；论证了系统闭环控制的可行性。由于磁悬浮轴承转子控制系统有别于一般的控制系统，不存在一般意义上的给定或跟踪信号的输入，即受控对象—转子不是对输入信号的响应，而是对扰动信号的抑制，在此基础上，以扰动信号为输入信号推导了系统的传递函数，在此基础上进行了仿真实验。
其次，论文应用神经网络PID控制对磁悬浮轴承转子系统进行了仿真研究。采用梯度下降法对网络的权值进行优化。分析了扰动对神经网络PID控制系统性能的影响，并对常规的PID控制和神经网络PID控制进行了比较。
第三，讨论了基于DSP的磁悬浮主轴的数字控制系统。研究了控制系统的硬件体系结构。并设计了基于DSP的磁悬浮轴承系统的神经网络PID控制系统的软件流程。
In this paper, the control system of the magnetic bearing is researched. The model of control system is founded from the disturb standpoint, and the characteristic of control system is analyzed. The theory of neural network is applied in the unit of single freedom magnetic bearing, the system of digital control is established around the core of the digital signal processor.
Firstly, the principle of the magnetic bearing is recommended briefly, the magnetic force is analyzed and calculated. The feasibility of the closed loop control of the rotor system supported on magnetic bearings is demonstrated. The control object- rotor is not the response of the input single, is the restrain of the disturbance input because that the control system of the rotor system supported on magnetic bearings is different from the general system, and does not exist the input of the importation single and the input of the track single, the transfer function of the system is founded with the disturbance single as the input single.
Secondly, the control of the neural network PID using in the rotor system supported on magnetic bearings is researched and simulated. The weigh-value of network is optimized using the method of grads-descending. This paper analysis the influence of the disturbance single upon the capability of the rotor system supported on magnetic bearings, and compares the difference of the control of general PID and the control of neural network
PID.
Thirdly, the digital control system of the rotor system supported on magnetic bearings based on DSP is designed. The hardware structure of the control system is researched. The basic game of the control system of neural network of the magnetic bearing system based on DSP is planed.
首先，本文简要介绍了磁悬浮轴承的工作原理，对电磁力进行了分析和计算；论证了系统闭环控制的可行性。由于磁悬浮轴承转子控制系统有别于一般的控制系统，不存在一般意义上的给定或跟踪信号的输入，即受控对象—转子不是对输入信号的响应，而是对扰动信号的抑制，在此基础上，以扰动信号为输入信号推导了系统的传递函数，在此基础上进行了仿真实验。
其次，论文应用神经网络PID控制对磁悬浮轴承转子系统进行了仿真研究。采用梯度下降法对网络的权值进行优化。分析了扰动对神经网络PID控制系统性能的影响，并对常规的PID控制和神经网络PID控制进行了比较。
第三，讨论了基于DSP的磁悬浮主轴的数字控制系统。研究了控制系统的硬件体系结构。并设计了基于DSP的磁悬浮轴承系统的神经网络PID控制系统的软件流程。
In this paper, the control system of the magnetic bearing is researched. The model of control system is founded from the disturb standpoint, and the characteristic of control system is analyzed. The theory of neural network is applied in the unit of single freedom magnetic bearing, the system of digital control is established around the core of the digital signal processor.
Firstly, the principle of the magnetic bearing is recommended briefly, the magnetic force is analyzed and calculated. The feasibility of the closed loop control of the rotor system supported on magnetic bearings is demonstrated. The control object- rotor is not the response of the input single, is the restrain of the disturbance input because that the control system of the rotor system supported on magnetic bearings is different from the general system, and does not exist the input of the importation single and the input of the track single, the transfer function of the system is founded with the disturbance single as the input single.
Secondly, the control of the neural network PID using in the rotor system supported on magnetic bearings is researched and simulated. The weigh-value of network is optimized using the method of grads-descending. This paper analysis the influence of the disturbance single upon the capability of the rotor system supported on magnetic bearings, and compares the difference of the control of general PID and the control of neural network
PID.
Thirdly, the digital control system of the rotor system supported on magnetic bearings based on DSP is designed. The hardware structure of the control system is researched. The basic game of the control system of neural network of the magnetic bearing system based on DSP is planed.
引文
[1] 李士勇，模糊控制神经控制和智能控制论．哈尔滨工业大学出版社，1997．6．
[2] 阎平凡，神经网络与模糊控制．清华大学出版社，1996．5．
[3] 欧阳黎明，Matlab控制系统设计．国防工业出版社．
[4] 孙增析，智能控制理论与技术．清华大学出版社、广西科学技术出版社．
[5] 丁祝顺等，磁悬浮轴承的研究进展．电力情报，No4，1994．
[6] 李士勇，复杂系统、非线性科学与智能控制理论．计算机自动测量与控制，2000．8(4)．
[7] 林治强，磁浮轴承控制的研究．学位论文，西安理工大学．
[8] 施阳等，主动磁悬浮轴承的神经网络PID控制研究．机械科学与技术，Vol．17 No．2 Mar 1998．
[9] 吴麒主编，自动控制理论．清华大学出版社．
[10] 王蓓蕾，谈振藩，DSP在数字控制系统设计中的应用．哈尔滨工程大学自动化学院．
[11] 施阳，李俊等，Matlab语言工具箱-Toolbox实用指南．西北工业大学出版社，1999．4．
[12] 楼顺天，施阳，基于MATLAB的系统分析与设计．西安电子科技大学出版社，2000．11．
[13] 薛定宇，控制系统计算机辅助设计-MATLAB语言及应用．清华大学出版社．1998．6．
[14] 张雄伟，曹铁勇，DSP芯片的原理与开发．电子工业出版社，1997．8．
[15] TMS630C542使用手册．TI公司编写．
[16] 张烽生，龚金宝，光电子器件应用基础．机械工业出版社．
[17] 高建臣，DSP在模糊神经网络控制器中的应用．北京理工大学机电一体化人才培训中心．
[18] 吕剑虹，智能PID控制器的设计及其应用研究．东南大学．
[19] 郑红，吴冠，54x DSP应用系统设计．北京航空航天大学出版社，2002．5．
[20] C. 施伟策，H. 布鲁勒，A. 特拉克斯勒 著；虞烈，袁崇军 译。主动磁轴承基础、性能及应用．新时代出版社．
[21] 涂时亮，张友德，单片微机控制技术．复旦大学出版社，1996．5．
[22] 张秀琼，吴定容，微型计算机原理及接口技术，北京科学技术出版社，1992．9．
[23] 胡广书，数字信号处理—理论算法与实现．清华大学出版社，1997．8．
[24] 杨天怡，黄勤，微型计算机控制技术．重庆大学出版社，1996．6．
[25] 新编功率晶体管实用手册—场效应管．电子工业部半导体专业情报网编，电子工业出版社．
[26] 杨立群，刘文彦，曹红亚，DSP与高速ADC接口的几种模式．电子技术，1999．3．
[27] 陈汝全，林水生，实用微机与单片机控制技术．电子科技大学出版社，1993．11．
[28] Kevin St, Dublin 8. PID compensation of time delayed process: a survey. Aidan O'Dwyer School of Control Systems and Electrical Engineering Dublin Institute of Technology.
[29] J. M. A. Scherpen, B. van der Kerkm, J. B. Klaassens, M. Lazeroms, S. Y. Kan Fac. of Inf. Nonlinear Control for Magnetic Bearings in Deployment Test Rigs: Simulation and Experimental
Results Techn. & Syst, Dept of Electrical Eng, Delft University of P. O. Box 5031, 2600 GA Delft, The Netherlands.
[30] KristerForsman, Anders Stenman. FuzzyCAT-Towards a Mathematical Analysis of Fuzzy Controllers: Jan-Erik Stromberg Linkoping University S-581 83 LinkopingSweden.
[31] Jelena Godjevac. Comparative study of fuzzy control, neural network control and neuro-fuzzy control: Technical Report no. 103/95 February 1995.
[32] Panagiotis Tsiotras. SELF-SCHEDULED H_∞ CONTROLLERS FORMAGNETIC BEARINGS: Stephen Mason Department of Mechanical Aerospace, and Nuclear Engineering University of Virginia, Charlottesville, VA 22903-1442.
[33] Bias Jayati Ghosh, Debargha Mukherjee, Michael Baloh Brad Paden. Nonlinear Control of a Benchmark Beam Balance Experiment Using Variable Hyperbolic: Department of Mechanical & Enviromnental Engineering UCSB, CA 93106, U. S. A.
[34] Jelena Godjevac. Comparison between PID and fuzzy control: Internal Report R93. 361 LAMI INF EPFL Ecublens 1015 Lausanne.
[35] 施阳，严卫生，任章，徐德民，主动磁悬浮轴承的神经网络 PID控制研究．机械科学与技术，Vod1．17，No．2 Mar 1998：336-341．
[36] 张显库，贾欣乐，求PID参数新方法．系统工程与电子技术，Vol．22，No．8 2000．
[37] 胡晚霞，余玲玲，戴义保，何亨文，PID控制器参数快速整定的新方法．自动化与仪器仪表，1996．5．
[38] 孙英，杨鹏，任娜，单轴磁悬浮系统控制器的仿真研究．河北工业大学学报，Vol．29 No．4，2000．8．
[39] 龙志强，金永德，磁悬浮轴承的控制系统设计．机电工程，1994．1．
[40] 胡业发，郭顺生，余先涛，杨明忠，数控磁悬浮智能转子系统的研究．武汉汽车工业大学学报，Vol．22 No．3，2000．7．
[41] 杜建凤，崔勇，张寒松，严忠慧，神经网络PID控制．北京科技大学学报，Vol．20 No．6，1998，12．
[42] 郭维钧，俞洪，吕炳仁，计算机控制磁力轴承-悬浮转子系统的研究．北京联合大学学报，Vol．8 No18，1994．4．
[43] 王耀南，童调生，神经网络智能PID参数最优控制及应用．湖南大学学报，Vol．21 No．2，1994．8．
[44] 曾励，黄民双，刘正埙，磁悬浮轴承的原理及现状．机械工艺学．
[45] 姜晓国，杨富贵，磁悬浮轴承控制系统中的电路分析及其试验研究．河北建筑科技学院学报，Vol．16 No．1，1999．3．
[46] Jelena Godjevac, Comparison between PID and fuzzy control: Ecole Polytechnique Federale de Lausanne Department d' Infornatique Laboratoire de Microinformatique. internal Report R93. 361. PROCEEDINGS OF THE EIGHTH INTERNATIONAL SYSMPOSIUM ON MAGNETIC BEARINGS, JAPAN, 2002.
[47] H. F. Steffani, W. Hofmann, B. Cebulski. A CONTROLLER FOR A MAGNETIC BEARING USING THE DYNAMIC PROGRAMMING METHOD OF BELLMAN: 569-576. PROCEEDINGS OF THE EIGHTH INTERNATIONAL SYMPOSUM ON MAGNETIC BEARINGS, JAPAN, 2002.
[48] Lyndon S. Stephens, Marc A. Timmerman, Mark A. Casemore. GAIN SCHEDULED PID CONTROL FOR ELECTROMAGNETIC VIBRATION ABSORBERS: 331-340. PROCEEDINGS OF THE EIGHTH INTERNATIONAL SYMPOSIUM ON MAGNETIC BEARINGS, JAPAN, 2002.
[49] Yohji Okada, Takashi Saitoh, Yoshihiko Shinoda. VIBRATION
CONTROL OF FLEXIBLE ROTOR SUPPORTED BY INCLINATION CONTROL MAGNETIC BEARINGS: 702-709. PROCEEDINGS OF THE EIGHTH INTERNATIONAL SYMPOSIUM ON MAGNETIC BEARINGS, JAPAN, 2002.
[50] Mathias Paul, Wilfried Hofmann, Hans Friedrich Steffani. COMPENSATION FOR UNBALANCES WITH AID OF NEURAL NETWORKS: 693-701. PROCEEDINGS OF THE EIGHTH INTERNATIONAL SYMPOSIUM ON MAGNETIC BEARING, JAPAN, 2002.
[51] Kenzo Nonami, Qi-fu Fan, Hirochika Ueyama. BEARING SYSTEMS USING ADAPTIVE ALGORITHM WITH DISTURBANCE FREQUENCY ESTIMATION: 663-673-2. PROCEEDINGS OF THE EIGHTH INTERNATIONAL SYMPOSIUM ON MAGNETIC BEARINGS, JAPAN, 2002.
[52] Jerome De Miras, Ali Charara. UNBALANCE CANCELLATION WITH ROTATING REFERENCE CONTROL FOR A HORIZONTAL SHAFT: 673-682. PROCEEDINGS OF THE EIGHTH INTERNATIONAL SYMPOSIUM ON MAGNETIC BEARINGS, JAPAN, 2002.
[53] P. Schroder, B. Green, N. Grum, P. J. Fleming. ON-LINE GENETIC AUTO-TUNING OF MAGNETIC BEARING CONTROLLERS: 321-330. PROCEEDINGS OF THE EIGHTH INTERNATIONAL SYMPOSIUM ON MAGNETIC BEARINGS, JAPAN, 2002.
[54] TEXAS INSTRUMENTS. TMS320C542X DSP Reference Set. Volume 1: CPU and Peripherals.
[55] TEXAS INSTRUMENTS. TMS320C542X DSP Reference Set. Volume 3: Algebraic Instruction Set.
[56] TEXAS INSTRUMENTS. TMS320C542X DSKplus. DSP Starter Kit.
[2] 阎平凡，神经网络与模糊控制．清华大学出版社，1996．5．
[3] 欧阳黎明，Matlab控制系统设计．国防工业出版社．
[4] 孙增析，智能控制理论与技术．清华大学出版社、广西科学技术出版社．
[5] 丁祝顺等，磁悬浮轴承的研究进展．电力情报，No4，1994．
[6] 李士勇，复杂系统、非线性科学与智能控制理论．计算机自动测量与控制，2000．8(4)．
[7] 林治强，磁浮轴承控制的研究．学位论文，西安理工大学．
[8] 施阳等，主动磁悬浮轴承的神经网络PID控制研究．机械科学与技术，Vol．17 No．2 Mar 1998．
[9] 吴麒主编，自动控制理论．清华大学出版社．
[10] 王蓓蕾，谈振藩，DSP在数字控制系统设计中的应用．哈尔滨工程大学自动化学院．
[11] 施阳，李俊等，Matlab语言工具箱-Toolbox实用指南．西北工业大学出版社，1999．4．
[12] 楼顺天，施阳，基于MATLAB的系统分析与设计．西安电子科技大学出版社，2000．11．
[13] 薛定宇，控制系统计算机辅助设计-MATLAB语言及应用．清华大学出版社．1998．6．
[14] 张雄伟，曹铁勇，DSP芯片的原理与开发．电子工业出版社，1997．8．
[15] TMS630C542使用手册．TI公司编写．
[16] 张烽生，龚金宝，光电子器件应用基础．机械工业出版社．
[17] 高建臣，DSP在模糊神经网络控制器中的应用．北京理工大学机电一体化人才培训中心．
[18] 吕剑虹，智能PID控制器的设计及其应用研究．东南大学．
[19] 郑红，吴冠，54x DSP应用系统设计．北京航空航天大学出版社，2002．5．
[20] C. 施伟策，H. 布鲁勒，A. 特拉克斯勒 著；虞烈，袁崇军 译。主动磁轴承基础、性能及应用．新时代出版社．
[21] 涂时亮，张友德，单片微机控制技术．复旦大学出版社，1996．5．
[22] 张秀琼，吴定容，微型计算机原理及接口技术，北京科学技术出版社，1992．9．
[23] 胡广书，数字信号处理—理论算法与实现．清华大学出版社，1997．8．
[24] 杨天怡，黄勤，微型计算机控制技术．重庆大学出版社，1996．6．
[25] 新编功率晶体管实用手册—场效应管．电子工业部半导体专业情报网编，电子工业出版社．
[26] 杨立群，刘文彦，曹红亚，DSP与高速ADC接口的几种模式．电子技术，1999．3．
[27] 陈汝全，林水生，实用微机与单片机控制技术．电子科技大学出版社，1993．11．
[28] Kevin St, Dublin 8. PID compensation of time delayed process: a survey. Aidan O'Dwyer School of Control Systems and Electrical Engineering Dublin Institute of Technology.
[29] J. M. A. Scherpen, B. van der Kerkm, J. B. Klaassens, M. Lazeroms, S. Y. Kan Fac. of Inf. Nonlinear Control for Magnetic Bearings in Deployment Test Rigs: Simulation and Experimental
Results Techn. & Syst, Dept of Electrical Eng, Delft University of P. O. Box 5031, 2600 GA Delft, The Netherlands.
[30] KristerForsman, Anders Stenman. FuzzyCAT-Towards a Mathematical Analysis of Fuzzy Controllers: Jan-Erik Stromberg Linkoping University S-581 83 LinkopingSweden.
[31] Jelena Godjevac. Comparative study of fuzzy control, neural network control and neuro-fuzzy control: Technical Report no. 103/95 February 1995.
[32] Panagiotis Tsiotras. SELF-SCHEDULED H_∞ CONTROLLERS FORMAGNETIC BEARINGS: Stephen Mason Department of Mechanical Aerospace, and Nuclear Engineering University of Virginia, Charlottesville, VA 22903-1442.
[33] Bias Jayati Ghosh, Debargha Mukherjee, Michael Baloh Brad Paden. Nonlinear Control of a Benchmark Beam Balance Experiment Using Variable Hyperbolic: Department of Mechanical & Enviromnental Engineering UCSB, CA 93106, U. S. A.
[34] Jelena Godjevac. Comparison between PID and fuzzy control: Internal Report R93. 361 LAMI INF EPFL Ecublens 1015 Lausanne.
[35] 施阳，严卫生，任章，徐德民，主动磁悬浮轴承的神经网络 PID控制研究．机械科学与技术，Vod1．17，No．2 Mar 1998：336-341．
[36] 张显库，贾欣乐，求PID参数新方法．系统工程与电子技术，Vol．22，No．8 2000．
[37] 胡晚霞，余玲玲，戴义保，何亨文，PID控制器参数快速整定的新方法．自动化与仪器仪表，1996．5．
[38] 孙英，杨鹏，任娜，单轴磁悬浮系统控制器的仿真研究．河北工业大学学报，Vol．29 No．4，2000．8．
[39] 龙志强，金永德，磁悬浮轴承的控制系统设计．机电工程，1994．1．
[40] 胡业发，郭顺生，余先涛，杨明忠，数控磁悬浮智能转子系统的研究．武汉汽车工业大学学报，Vol．22 No．3，2000．7．
[41] 杜建凤，崔勇，张寒松，严忠慧，神经网络PID控制．北京科技大学学报，Vol．20 No．6，1998，12．
[42] 郭维钧，俞洪，吕炳仁，计算机控制磁力轴承-悬浮转子系统的研究．北京联合大学学报，Vol．8 No18，1994．4．
[43] 王耀南，童调生，神经网络智能PID参数最优控制及应用．湖南大学学报，Vol．21 No．2，1994．8．
[44] 曾励，黄民双，刘正埙，磁悬浮轴承的原理及现状．机械工艺学．
[45] 姜晓国，杨富贵，磁悬浮轴承控制系统中的电路分析及其试验研究．河北建筑科技学院学报，Vol．16 No．1，1999．3．
[46] Jelena Godjevac, Comparison between PID and fuzzy control: Ecole Polytechnique Federale de Lausanne Department d' Infornatique Laboratoire de Microinformatique. internal Report R93. 361. PROCEEDINGS OF THE EIGHTH INTERNATIONAL SYSMPOSIUM ON MAGNETIC BEARINGS, JAPAN, 2002.
[47] H. F. Steffani, W. Hofmann, B. Cebulski. A CONTROLLER FOR A MAGNETIC BEARING USING THE DYNAMIC PROGRAMMING METHOD OF BELLMAN: 569-576. PROCEEDINGS OF THE EIGHTH INTERNATIONAL SYMPOSUM ON MAGNETIC BEARINGS, JAPAN, 2002.
[48] Lyndon S. Stephens, Marc A. Timmerman, Mark A. Casemore. GAIN SCHEDULED PID CONTROL FOR ELECTROMAGNETIC VIBRATION ABSORBERS: 331-340. PROCEEDINGS OF THE EIGHTH INTERNATIONAL SYMPOSIUM ON MAGNETIC BEARINGS, JAPAN, 2002.
[49] Yohji Okada, Takashi Saitoh, Yoshihiko Shinoda. VIBRATION
CONTROL OF FLEXIBLE ROTOR SUPPORTED BY INCLINATION CONTROL MAGNETIC BEARINGS: 702-709. PROCEEDINGS OF THE EIGHTH INTERNATIONAL SYMPOSIUM ON MAGNETIC BEARINGS, JAPAN, 2002.
[50] Mathias Paul, Wilfried Hofmann, Hans Friedrich Steffani. COMPENSATION FOR UNBALANCES WITH AID OF NEURAL NETWORKS: 693-701. PROCEEDINGS OF THE EIGHTH INTERNATIONAL SYMPOSIUM ON MAGNETIC BEARING, JAPAN, 2002.
[51] Kenzo Nonami, Qi-fu Fan, Hirochika Ueyama. BEARING SYSTEMS USING ADAPTIVE ALGORITHM WITH DISTURBANCE FREQUENCY ESTIMATION: 663-673-2. PROCEEDINGS OF THE EIGHTH INTERNATIONAL SYMPOSIUM ON MAGNETIC BEARINGS, JAPAN, 2002.
[52] Jerome De Miras, Ali Charara. UNBALANCE CANCELLATION WITH ROTATING REFERENCE CONTROL FOR A HORIZONTAL SHAFT: 673-682. PROCEEDINGS OF THE EIGHTH INTERNATIONAL SYMPOSIUM ON MAGNETIC BEARINGS, JAPAN, 2002.
[53] P. Schroder, B. Green, N. Grum, P. J. Fleming. ON-LINE GENETIC AUTO-TUNING OF MAGNETIC BEARING CONTROLLERS: 321-330. PROCEEDINGS OF THE EIGHTH INTERNATIONAL SYMPOSIUM ON MAGNETIC BEARINGS, JAPAN, 2002.
[54] TEXAS INSTRUMENTS. TMS320C542X DSP Reference Set. Volume 1: CPU and Peripherals.
[55] TEXAS INSTRUMENTS. TMS320C542X DSP Reference Set. Volume 3: Algebraic Instruction Set.
[56] TEXAS INSTRUMENTS. TMS320C542X DSKplus. DSP Starter Kit.