基于FPGA的永磁同步电机参数辨识的研究
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摘要
永磁同步电机(PMSM)因其结构简单、体积小、重量轻、损耗小、效率高和恒功率运行时调速范围宽等特点,在高效能驱动系统中应用广泛。永磁同步电机伺服系统中,转动惯量的变化对系统的机械性能有较大的影响,如能及时地辨识出转动惯量,并根据辨识结果对伺服系统的控制器参数进行相应调整,使伺服系统具有更好的控制特性,将对永磁同步电机伺服系统的应用具有重要意义。本文在研究已有辨识算法的基础上,对PMSM的转动惯量的辨识方案进行了深入研究,提出了改进型方案,并用Matlab和SG实现了辨识算法的仿真验证。
首先,本文在对PMSM的数学模型进行分析的基础上,提出了一种新型的模糊PI控制器,基于Matlab/Simulink的仿真结果表明:与永磁同步电机PI控制伺服系统相比,永磁同步电机模糊PI控制系统鲁棒性强、响应快、对被控对象参数变化具有较好的适应能力,但是其性能仍然受PMSM转动惯量变化的影响较大。为此开展了对PMSM转动惯量辨识的一系列研究工作。分析了遗忘因子最小二乘法和模型参考自适应法在永磁同步电机转动惯量辨识中应用,针对带遗忘因子的最小二乘法辨识结果波动大的问题,引入开关控制器,提出了一种新型的最小二乘参数辨识器,当转动惯量发生变化时,通过开关控制对辨识器进行初始化,实现了对参数更快更好的在线辨识。仿真实验表明:该方法克服了传统的带遗忘因子的最小二乘辨识器的波动现象,辨识速度快,精度高,可以有效地提高系统的性能。在模型参考自适应算法的基础上,引入辨识结果的反馈,根据辨识结果的状态实现增益的自适应调节,从而避免参数变化时辨识结果的波动,仿真实验表明:该方法消弱了原辨识算法在辨识时变参数时的波动,能更快的跟踪转动惯量的变化,表现出更好的辨识特性。最后基于SG仿真工具,对改进型辨识器的性能进行了仿真验证,为永磁同步电机的FPGA参数辨识器的设计奠定了基础。
Permanent magnet synchronous motor(PMSM) is an attractive candidates for high performance drive systems because of its high efficiency and suitability for wide speed ranges of constant power operation. The performance of PMSM servo system is highly influenced by variation of moment of inertia. In order to enhance the system performances, it is necessary to identify the moment of inertia and adjust the controller’s parameters. Based on the traditional algorithm, the improved identification algorithms are proposed in the paper. The performances of the improved identification algorithms are verified in Matlab and System Generator.
Firstly, based on the analysis of PMSM mathematical model, a novel fuzzy PI controller is proposed. Simulations based on Matlab/Simulink are carried out and the results show that when comparing to the traditional PI control strategy, systems with the proposed controller characterized by performances of good robustness to disturbances and parameter varieties, quick response and better stability, but it is still affected largely by the variable moment of inertia. Then moment of inertia identification research is done in this paper. The Recursive Least Squares(RLS) algorithm and model reference adaptive identification algorithm are discoursed. On the basis of RLS algorithm, a new identifier based on the Recursive Least Squares algorithm is proposed. A switch controller is introduced to the Forgetting Factor Recursive Least Squares(FFRLS) identifier to overcome the fluctuation. When the moment of the inertia changes, the switch controller initialize the identifier and realize the quick tracking of the parameter. The simulation and experiments show that this method is free from the fluctuation, the identifying is more quickly and high precision, and is benefit for the improvement of the performance of the system. To reduce the fluctuation, on the basis of mode1 reference adaptive identification algorithm, the identification result is feedbacked. In the process of the identification, the larger adaptive gain is chosen to identify the parameter quickly; when the result of identification is steady, the smaller adaptive gain is choosen. Then the improved identification algorithms is used to identify PMSM moment of inertia, the simulation and experiments show that the improved algorithms are free from fluctuation, the identifications are more quickly and high precision. Finally, the models of the improved algorithms are established with the System Generator, and give good validation for the proposed algorithms. These works built the foundation of the hardware realization based on the FPGA.
首先,本文在对PMSM的数学模型进行分析的基础上,提出了一种新型的模糊PI控制器,基于Matlab/Simulink的仿真结果表明:与永磁同步电机PI控制伺服系统相比,永磁同步电机模糊PI控制系统鲁棒性强、响应快、对被控对象参数变化具有较好的适应能力,但是其性能仍然受PMSM转动惯量变化的影响较大。为此开展了对PMSM转动惯量辨识的一系列研究工作。分析了遗忘因子最小二乘法和模型参考自适应法在永磁同步电机转动惯量辨识中应用,针对带遗忘因子的最小二乘法辨识结果波动大的问题,引入开关控制器,提出了一种新型的最小二乘参数辨识器,当转动惯量发生变化时,通过开关控制对辨识器进行初始化,实现了对参数更快更好的在线辨识。仿真实验表明:该方法克服了传统的带遗忘因子的最小二乘辨识器的波动现象,辨识速度快,精度高,可以有效地提高系统的性能。在模型参考自适应算法的基础上,引入辨识结果的反馈,根据辨识结果的状态实现增益的自适应调节,从而避免参数变化时辨识结果的波动,仿真实验表明:该方法消弱了原辨识算法在辨识时变参数时的波动,能更快的跟踪转动惯量的变化,表现出更好的辨识特性。最后基于SG仿真工具,对改进型辨识器的性能进行了仿真验证,为永磁同步电机的FPGA参数辨识器的设计奠定了基础。
Permanent magnet synchronous motor(PMSM) is an attractive candidates for high performance drive systems because of its high efficiency and suitability for wide speed ranges of constant power operation. The performance of PMSM servo system is highly influenced by variation of moment of inertia. In order to enhance the system performances, it is necessary to identify the moment of inertia and adjust the controller’s parameters. Based on the traditional algorithm, the improved identification algorithms are proposed in the paper. The performances of the improved identification algorithms are verified in Matlab and System Generator.
Firstly, based on the analysis of PMSM mathematical model, a novel fuzzy PI controller is proposed. Simulations based on Matlab/Simulink are carried out and the results show that when comparing to the traditional PI control strategy, systems with the proposed controller characterized by performances of good robustness to disturbances and parameter varieties, quick response and better stability, but it is still affected largely by the variable moment of inertia. Then moment of inertia identification research is done in this paper. The Recursive Least Squares(RLS) algorithm and model reference adaptive identification algorithm are discoursed. On the basis of RLS algorithm, a new identifier based on the Recursive Least Squares algorithm is proposed. A switch controller is introduced to the Forgetting Factor Recursive Least Squares(FFRLS) identifier to overcome the fluctuation. When the moment of the inertia changes, the switch controller initialize the identifier and realize the quick tracking of the parameter. The simulation and experiments show that this method is free from the fluctuation, the identifying is more quickly and high precision, and is benefit for the improvement of the performance of the system. To reduce the fluctuation, on the basis of mode1 reference adaptive identification algorithm, the identification result is feedbacked. In the process of the identification, the larger adaptive gain is chosen to identify the parameter quickly; when the result of identification is steady, the smaller adaptive gain is choosen. Then the improved identification algorithms is used to identify PMSM moment of inertia, the simulation and experiments show that the improved algorithms are free from fluctuation, the identifications are more quickly and high precision. Finally, the models of the improved algorithms are established with the System Generator, and give good validation for the proposed algorithms. These works built the foundation of the hardware realization based on the FPGA.
引文
[1]刘锦波,张承慧等.电机与拖动[M].北京:清华大学出版社,2006.353-365
[2] Mohamed Boussak, Kamel Jarray. A High-Performance Sensorless Indirect Stator Flux Orientation Control of Induction Motor Drive[J]. IEEE Trans . Industrial Electronics,2006,53(1):41-49.
[3] Hossein Madadi Kojabadi, Liuchen Chang. A MRAS-Based Adaptive Pseudoreduced-Order Flux Observer for Sensorless Induction Motor Drives[J]. IEEE Trans . Industrial Electronics. 2005,20(4):930-938.
[4] Young Ahn Kwon. A New Scheme for Speed-Sensorless Control of Induction Motor[J]. IEEE Trans . Industrial Electronics.2004,51(3):545-550.
[5] A.Makouf, M.E.H. Benbouzid. A Practical Scheme for Induction Motor Speed Sensorless Field-Oriented Control[J]. IEEE Trans. Industrial Electronics.2004,19(1): 230-232.
[6] Haithem Abu-Rub. An Advanced Low-Cost Sensorless Induction Motor Drive[J]. IEEE Trans.Ind . Applicat.2003,39(6):1757-1764.
[7] Cheng-Hung Tsai. CMAC-Based Speed Estimation Method for Sensorless Vector Control of Induction Motor Drive[J]. Electric Power Components and Systems.2006, 34:1213–1230.
[8] Jorma Luomi. Observer Using Low-Frequency Injection for Sensorless Induction Motor Control—Parameter Sensitivity Analysis[J]. IEEE Trans. Industrial Electronics. 2006,53(1):216-224
[9]周海方,王雪帆.电机转子转动惯量的测量[J].电气技术,2006, (12):37-40.
[10]万敏,窦曰轩.异步电机全维速度观测器若干参数变化的影响[J].电机与控制学报,2008,12(1):15-19.
[11] Bornard Guy,Hammouri Hassan.A high gain observer for a class of uniformly observable systems[J].IEEE Pro.of the 30th Conf.on Decision and Control .1991(1):494-496.
[12] Solsona Jorge A. Disturbance and nonlinear Luenberger observers for estimating mechanical variables in Permanent Magnet Synchronous Motors under mechanical parameters uncertainties[J].IEEE Trans.on Ind.Electronics. 2003,50(4):717-725.
[13] Low Teck-Seng,Lee Tong-Heng,Chang Kuan-Teck.A Nonlinear speed observer for Permanent-Magnet Synchronous Motors[J].IEEE Trans.on Industrial Electronics. 1993,40(3):307-316.
[14] Elbuluk Malik,Li Changsheng.Sliding mode observer for wide-speed sensorless control of PMSM Drives[C].IEEE Ind.Appl.Conf.2003.38th IAS Annual Meeting. 2003(1):480-485.
[15] Ibarra-Rojas Sebastian,Moreno Jaime,Espinosa-Pérez Gerardo.Global observability analysis of sensorless induction motors[J].Automatica .2004,40:1079-1085.
[16]郑泽东,李永东,Maurice Fadel等.基于扩展Kalman滤波器的PMSM高性能控制系统[J].电工技术学报,2007,22(10):18-23.
[17] BOLOGNANI S,TUBIANA L,ZIGLIOTTO M.Extended Kalman filter tuning insensorless PMSM drives[J].IEEE Trans.on Industry Applications.2003,39(6):1741-1747.
[18]余佩琼,陆亿红,王涌等.永磁直线同步电动机无位置传感器控制系统的研究[J].中国电机工程学报,2007,27(24):53-57.
[19] K L shi,T F chan,S L Ho.Speed Estimation of an Induction Motor Drive Using an Optimized Extended Kalman Filter[J].IEEE Trans.on Electronics.2002,(49):124-133.
[20]林平,胡长生,李明峰等.基于模型参考自适应系统算法的速度估算核的研制[J].中国电机工程学报,2004,24(1):118-123.
[21] Ide K,Mumkita I.Finite element analysis of sensodess induction machine by high frequency voltage injection[C].Proceedings of IPEC,Tokyo,2000:1842-1847.
[22]祝龙记,王宾.基于MRAS速度辨识矢量控制系统的仿真研究[J].电工技术学报,2005,20(1):60-65.
[23]梁艳.基于模型参考自适应的永磁同步电机无传感器控制研究[D]:[博士学位论文].北京:清华大学,2002.
[24] Ding F, Shi Y, Chen T. Auxiliary model based least-squares identification methods for Hammerstein output-error systems[J]. Systems & Control Letters, 2007, 56(5): 373-380.
[25] Ding F, Chen HB, Li M. Multi-innovation least squares identification methods based on the auxiliary model for MISO systems[J]. Applied Mathematics and Computation, 2007, 187(2):658-668.
[26] Ding F, Y.S. Xiao. A finite-data-window least squares algorithm with a forgetting factor for dynamical modeling[J]. Applied Mathematics and Computation,2007, 186(1):184-192.
[27] Ding F, Chen T. Performance bounds of forgetting factor least squares algorithm for time-varying systems with finite measurement data[J]. IEEE Transactions on Circuits and Systems, 2005, 52(3):555-566.
[28] Mei Hong, Torsten S?derstr?m, and Wei Xing Zheng .A Simplified form of the bias-eliminating least squares method for errors-in-variables identification[J]. IEEE transactions on automatic control, 2007, 52(9): 1754-1756.
[29]韩曾晋.自适应控制[M].北京:清华大学出版社,1995.77-92.
[30]郎宝华,刘卫国,周熙炜等.基于定子磁链全维状态观测器的直接转矩控制[J].电力电子技术,2007,41(11):29-33.
[31]高扬,杨明,于泳,徐殿国.基于扰动观测器的PMSM交流伺服系统低速控制[J].中国电机工程学报,2005,25(22):125-129.
[32]安群涛,孙力,李波.永磁同步电动机转速的变参数EKF估计方法[J].电机与控制学报,2007,11(6):559-563
[33]江俊,沈艳霞,纪志成.基于EKF的永磁同步电机转子位置和速度估计[J].系统仿真学报,2005,17(7):1704-1707.
[34] Y. Guo, L. Huang, Y. Qiu. Inertia identification and auto-tuning of induction motor using MRAS[C].Proceeding of the 3rd Power Electronics and Motion Control Conference, shanghai, China, 2000: 1006-1011.
[35]黄立培,邱阳,郭宇婕.异步电机转动惯量在线辨识与自适应控制的研究[J].电工电能新技术,2001, 20(1):11-15.
[36]王庆龙,张崇巍,张兴.交流电机无速度传感器矢量控制系统变结构模型参考自适应转速辨识[J].中国电机工程学报,2007,27(15):70-74.
[37]齐放,邓智泉,仇志坚等.基于MRAS的永磁同步电机无速度传感器[J].电工技术学报,2007,22(4):53-58.
[38]韩林.电机参数检测与辨识[D]:[硕士学位论文].杭州:浙江大学,2004.
[39]刘蜀宁.异步电机参数的在线辨识研究[D]:[硕士学位论文].成都:四川大学,2001.
[40]王振永,王然冉.电机的数学模型和参数辨识[M].北京:机械工业出版社,1989.75-106.
[41]刘辉.交流伺服系统及参数辨识算法研究[D]:[硕士学位论文].南京:南京航空航天大学,2005.
[42] Junliu, PuSheng Wu, HuaYu Bai, et al, Application of Fuzzy Control indirect Torque Control of Permanent Magnet Synchronous Motor[C]. Proceedings of the 5th World Congress on Intelligent Control and Automation, Hangzhou, P.R.China. 2004: 4573-4576.
[43] Jordi Catala, Luis Romeral, Novel Fuzzy Adaptive Sensorless Induction Motor Drive[J]. IEEE Transactions on Industrial Electronics, 2006,53(4): 1170-1178.
[44] LI Wei. Design of a hybrid fuzzy logic proportional plus conventional integral-derivative controller [J]. IEEE Trans. on Fuzzy systems, 1998, 4 (6):449 -463.
[45]纪志成,薛花,沈艳霞.永磁同步电动机调速系统的模糊PI智能控制新方法[J].电工技术学报,2003,18(6):53-58.
[46]丁锋.自适应控制系统[M].北京:清华大学出版社,2002.
[47] I D Landau, et al. Adaptive Control[M]. NewYork: Springer,1998.
[2] Mohamed Boussak, Kamel Jarray. A High-Performance Sensorless Indirect Stator Flux Orientation Control of Induction Motor Drive[J]. IEEE Trans . Industrial Electronics,2006,53(1):41-49.
[3] Hossein Madadi Kojabadi, Liuchen Chang. A MRAS-Based Adaptive Pseudoreduced-Order Flux Observer for Sensorless Induction Motor Drives[J]. IEEE Trans . Industrial Electronics. 2005,20(4):930-938.
[4] Young Ahn Kwon. A New Scheme for Speed-Sensorless Control of Induction Motor[J]. IEEE Trans . Industrial Electronics.2004,51(3):545-550.
[5] A.Makouf, M.E.H. Benbouzid. A Practical Scheme for Induction Motor Speed Sensorless Field-Oriented Control[J]. IEEE Trans. Industrial Electronics.2004,19(1): 230-232.
[6] Haithem Abu-Rub. An Advanced Low-Cost Sensorless Induction Motor Drive[J]. IEEE Trans.Ind . Applicat.2003,39(6):1757-1764.
[7] Cheng-Hung Tsai. CMAC-Based Speed Estimation Method for Sensorless Vector Control of Induction Motor Drive[J]. Electric Power Components and Systems.2006, 34:1213–1230.
[8] Jorma Luomi. Observer Using Low-Frequency Injection for Sensorless Induction Motor Control—Parameter Sensitivity Analysis[J]. IEEE Trans. Industrial Electronics. 2006,53(1):216-224
[9]周海方,王雪帆.电机转子转动惯量的测量[J].电气技术,2006, (12):37-40.
[10]万敏,窦曰轩.异步电机全维速度观测器若干参数变化的影响[J].电机与控制学报,2008,12(1):15-19.
[11] Bornard Guy,Hammouri Hassan.A high gain observer for a class of uniformly observable systems[J].IEEE Pro.of the 30th Conf.on Decision and Control .1991(1):494-496.
[12] Solsona Jorge A. Disturbance and nonlinear Luenberger observers for estimating mechanical variables in Permanent Magnet Synchronous Motors under mechanical parameters uncertainties[J].IEEE Trans.on Ind.Electronics. 2003,50(4):717-725.
[13] Low Teck-Seng,Lee Tong-Heng,Chang Kuan-Teck.A Nonlinear speed observer for Permanent-Magnet Synchronous Motors[J].IEEE Trans.on Industrial Electronics. 1993,40(3):307-316.
[14] Elbuluk Malik,Li Changsheng.Sliding mode observer for wide-speed sensorless control of PMSM Drives[C].IEEE Ind.Appl.Conf.2003.38th IAS Annual Meeting. 2003(1):480-485.
[15] Ibarra-Rojas Sebastian,Moreno Jaime,Espinosa-Pérez Gerardo.Global observability analysis of sensorless induction motors[J].Automatica .2004,40:1079-1085.
[16]郑泽东,李永东,Maurice Fadel等.基于扩展Kalman滤波器的PMSM高性能控制系统[J].电工技术学报,2007,22(10):18-23.
[17] BOLOGNANI S,TUBIANA L,ZIGLIOTTO M.Extended Kalman filter tuning insensorless PMSM drives[J].IEEE Trans.on Industry Applications.2003,39(6):1741-1747.
[18]余佩琼,陆亿红,王涌等.永磁直线同步电动机无位置传感器控制系统的研究[J].中国电机工程学报,2007,27(24):53-57.
[19] K L shi,T F chan,S L Ho.Speed Estimation of an Induction Motor Drive Using an Optimized Extended Kalman Filter[J].IEEE Trans.on Electronics.2002,(49):124-133.
[20]林平,胡长生,李明峰等.基于模型参考自适应系统算法的速度估算核的研制[J].中国电机工程学报,2004,24(1):118-123.
[21] Ide K,Mumkita I.Finite element analysis of sensodess induction machine by high frequency voltage injection[C].Proceedings of IPEC,Tokyo,2000:1842-1847.
[22]祝龙记,王宾.基于MRAS速度辨识矢量控制系统的仿真研究[J].电工技术学报,2005,20(1):60-65.
[23]梁艳.基于模型参考自适应的永磁同步电机无传感器控制研究[D]:[博士学位论文].北京:清华大学,2002.
[24] Ding F, Shi Y, Chen T. Auxiliary model based least-squares identification methods for Hammerstein output-error systems[J]. Systems & Control Letters, 2007, 56(5): 373-380.
[25] Ding F, Chen HB, Li M. Multi-innovation least squares identification methods based on the auxiliary model for MISO systems[J]. Applied Mathematics and Computation, 2007, 187(2):658-668.
[26] Ding F, Y.S. Xiao. A finite-data-window least squares algorithm with a forgetting factor for dynamical modeling[J]. Applied Mathematics and Computation,2007, 186(1):184-192.
[27] Ding F, Chen T. Performance bounds of forgetting factor least squares algorithm for time-varying systems with finite measurement data[J]. IEEE Transactions on Circuits and Systems, 2005, 52(3):555-566.
[28] Mei Hong, Torsten S?derstr?m, and Wei Xing Zheng .A Simplified form of the bias-eliminating least squares method for errors-in-variables identification[J]. IEEE transactions on automatic control, 2007, 52(9): 1754-1756.
[29]韩曾晋.自适应控制[M].北京:清华大学出版社,1995.77-92.
[30]郎宝华,刘卫国,周熙炜等.基于定子磁链全维状态观测器的直接转矩控制[J].电力电子技术,2007,41(11):29-33.
[31]高扬,杨明,于泳,徐殿国.基于扰动观测器的PMSM交流伺服系统低速控制[J].中国电机工程学报,2005,25(22):125-129.
[32]安群涛,孙力,李波.永磁同步电动机转速的变参数EKF估计方法[J].电机与控制学报,2007,11(6):559-563
[33]江俊,沈艳霞,纪志成.基于EKF的永磁同步电机转子位置和速度估计[J].系统仿真学报,2005,17(7):1704-1707.
[34] Y. Guo, L. Huang, Y. Qiu. Inertia identification and auto-tuning of induction motor using MRAS[C].Proceeding of the 3rd Power Electronics and Motion Control Conference, shanghai, China, 2000: 1006-1011.
[35]黄立培,邱阳,郭宇婕.异步电机转动惯量在线辨识与自适应控制的研究[J].电工电能新技术,2001, 20(1):11-15.
[36]王庆龙,张崇巍,张兴.交流电机无速度传感器矢量控制系统变结构模型参考自适应转速辨识[J].中国电机工程学报,2007,27(15):70-74.
[37]齐放,邓智泉,仇志坚等.基于MRAS的永磁同步电机无速度传感器[J].电工技术学报,2007,22(4):53-58.
[38]韩林.电机参数检测与辨识[D]:[硕士学位论文].杭州:浙江大学,2004.
[39]刘蜀宁.异步电机参数的在线辨识研究[D]:[硕士学位论文].成都:四川大学,2001.
[40]王振永,王然冉.电机的数学模型和参数辨识[M].北京:机械工业出版社,1989.75-106.
[41]刘辉.交流伺服系统及参数辨识算法研究[D]:[硕士学位论文].南京:南京航空航天大学,2005.
[42] Junliu, PuSheng Wu, HuaYu Bai, et al, Application of Fuzzy Control indirect Torque Control of Permanent Magnet Synchronous Motor[C]. Proceedings of the 5th World Congress on Intelligent Control and Automation, Hangzhou, P.R.China. 2004: 4573-4576.
[43] Jordi Catala, Luis Romeral, Novel Fuzzy Adaptive Sensorless Induction Motor Drive[J]. IEEE Transactions on Industrial Electronics, 2006,53(4): 1170-1178.
[44] LI Wei. Design of a hybrid fuzzy logic proportional plus conventional integral-derivative controller [J]. IEEE Trans. on Fuzzy systems, 1998, 4 (6):449 -463.
[45]纪志成,薛花,沈艳霞.永磁同步电动机调速系统的模糊PI智能控制新方法[J].电工技术学报,2003,18(6):53-58.
[46]丁锋.自适应控制系统[M].北京:清华大学出版社,2002.
[47] I D Landau, et al. Adaptive Control[M]. NewYork: Springer,1998.