基于神经网络的感应电机矢量控制系统研究
|
摘要
近年来,随着电力电子器件、微处理器和电机控制理论的飞速发展,感应电机在运动控制中得到了广泛应用,与此同时,人们对其性能要求也越来越高。一方面,由于感应电机矢量控制系统会受电机参数变化以及外部负载扰动等因素的影响,单纯采用传统控制方法难以满足高性能的控制要求,因此需要谋求新的控制策略以使系统对这类影响具有较好的鲁棒性。另一方面,在转速闭环控制的高性能感应电机矢量控制系统中,速度传感器的安装给系统带来了一些不利的影响。研究无速度传感器控制系统并改善其性能,成为交流传动领域的研究热点。本文考虑到神经网络在非线性、复杂系统以及不确定系统的控制与辨识方面优越的性能,将它分别与PI控制以及模型参考自适应控制相结合,应用到感应电机有速度传感器和无速度传感器矢量控制系统中,改善了系统的性能。
首先,在有速度传感器矢量控制系统中,为解决在系统参数发生较大变化等情况时,采用常规PI控制器难以获得满意的控制性能的问题、减轻系统参数变化和外部负载扰动对系统产生的不良影响,本文根据神经网络和矢量控制系统的特点,将神经网络控制理论分别与PI控制和模型参考自适应控制相结合,组成了具有自适应能力的神经网络PI速度控制器和神经网络模型参考补偿控制器,实现了矢量控制系统对电机参数及负载转矩等的自适应控制。
在神经网络模型参考补偿控制器的具体应用中,为提高网络的映射能力和矢量控制系统的动态响应能力,本文在控制器中采用三层前向神经网络结构,并用广义PID神经网络自适应算法修正网络权值,采用这种算法在一定程度上加快了网络的收敛速度。
随后,在无速度传感器矢量控制系统中,本文探讨了基于神经网络PI控制器的磁链观测方法、定子电阻在线辨识方法和转速估算方法。
在转子磁场定向的无速度传感器矢量控制系统中,为保证矢量解耦控制的有效实现,需要进行精确的磁场定向。在众多磁通观测方法中,转子磁链的电压模型和电流模型各有优缺点且在很多方面具有互补性,因此很多方法利用电压-电流组合模型进行磁链观测。本文在静止坐标系下,探讨了一种基于神经网络PI控制器的电压-电流组合模型闭环磁通观测方法,通过电流模型对电压模型的自适应修正作用将二者结合起来完成转子磁链的观测过程,解决了电压模型观测器的不稳定问题。
在组合模型闭环磁链观测器中,随温度变化的定子电阻变化会直接影响转子磁链的观测效果,进而影响转速的控制精度,破坏系统的性能。为此,本文探讨了用神经网络PI控制器实现的定子电阻在线辨识方法,通过定子电阻在线辨识提高了转速控制精度。
在无速度传感器矢量控制系统的核心问题——转速估算环节中,为改进基于PI控制作用构造转速信号的系统的性能,本文探讨了一种基于神经网络PI控制器的转速估算方法。该方法将定参数PI调节器的PI参数直接作为神经网络权值,在输入偏差变化时,通过对权值PI参数的自适应调整,加快了系统的收敛速度、减小了超调量和稳定时间。
最后,本文在基于TMS320F2808的感应电机矢量控制系统实验平台上,进行了有速度传感器和无速度传感器矢量控制实验。在有速度传感器矢量控制实验中,采用神经网络PI速度控制器进行转速调节,证实了控制方案的有效性和可实现性。对于无速度传感器矢量控制,用估算转速进行闭环控制,验证了本文探讨的基于神经网络PI控制器的转速估算方法能有效地改善系统的动态性能,在突加负载扰动和低速运行时能保证系统稳定运行,同时验证了采用的逆变器死区电压补偿方法能有效改善电流波形,改进系统性能。
In recent years, induction motor has been extensively used in motion-control field with the advances in power devices, micro-processor and motor control theory. At the same time, its high performance is required increasingly. On one hand, as the performance of induction motor vector control system will be influenced by parameter variations, external load disturbances, etc., the high performance requirement can’t be met simply using traditional control methods. To make the system be robust to the influences, new control strategies have to be developed. On the other hand, the mounting of speed sensors brings some disadvantages to the induction motor vector control system in which rotor speed is closed-loop controlled. Therefore, the research to speed-sensorless drive system and enhancing its performance has become a popular research aspect in AC drive field. Considering the superior performance in the control and identification to nonlinear, complex and uncertain systems, neural networks was used in induction motor vector control system combined with PI control and model reference adaptive control in this dissertation, which has improved system performance.
At first, in order to solve the problem that satisfactory performance can’t be achieved using conventional PI controller when the system parameters vary in a wide range and reduce the influence of parameter variations and external load disturbances, the adaptive neural network PI speed controller and neural network model reference compensation controller were used in induction motor vector control system in the dissertation. The controller was constructed according to the characteristic of neural networks and vector control system and combining neural network with PI control and model reference adaptive control. Then, adaptive control to motor parameters and the load torque was realized.
In the application of the neural network model reference compensation controller, to enhance the mapping capability and dynamic response capability of the system, a three-layer forward neural network was used in the controller and adaptive control algorithm with generalized PID was used to correct the weights of the network. In a certain extent, the speed of convergence was accelerated by using the algorithm.
Subsequently, the flux observation method, stator resistance on-line identification method and speed estimation method based on the neural network PI controller were presented in the speed-sensorless vector control system.
In the rotor flux-oriented system, in order to guarantee the effective decoupling of vector control, the precise flux orientation is needed. Among many flux observation methods, voltage model and current model have their own advantages and disadvantages, and they are complementary in many ways, so they were combined in many flux observers. A closed-loop flux observation method using voltage-current combination model based on the neural network PI controller was presented in the dissertation, which realized the rotor flux observation and solved the instability of voltage model through the current model’s adaptive correction to the voltage model in stationary axes,.
In closed-loop flux observer using combination model, the stator resistance’s error by the temperature’s variation will impact on the accuracy of rotor flux observer and speed control, destroy the performance of the system. Therefore stator resistance on-line identification method based on neural network PI controller was presented, by which the accuracy of speed control was improved.
About the core of the speed-sensorless vector control system, that is speed estimation, in order to improve the performance of the system, in which the speed signal is structured with PI control, a speed estimation method based on the neural network PI controller was presented. In the method, the PI parameters were set as the weights of the neural network. When the input error varied, the weights were adaptively regulated, by which the convergence rate was accelerated, at the same time, the overshoot and stabilization time were reduced.
Finally, the experiments were done in the induction motor vector control system platform based on TMS320F2808. In the system with speed sensor, neural network PI controller was used for speed control. The experiment results verify the program is valid and feasible. In the system without speed sensor, the estimated speed is closed-loop controlled. The experiment results verify that system’s dynamic performance can be improved by using the speed estimation method based on neural network PI controller presented in the dissertation, the stability of the system was guaranteed in sudden increase in load disturbance and low-speed operation, the current waveform and system performance can be improved effectively by using the dead zone inverter voltage compensation method.
首先,在有速度传感器矢量控制系统中,为解决在系统参数发生较大变化等情况时,采用常规PI控制器难以获得满意的控制性能的问题、减轻系统参数变化和外部负载扰动对系统产生的不良影响,本文根据神经网络和矢量控制系统的特点,将神经网络控制理论分别与PI控制和模型参考自适应控制相结合,组成了具有自适应能力的神经网络PI速度控制器和神经网络模型参考补偿控制器,实现了矢量控制系统对电机参数及负载转矩等的自适应控制。
在神经网络模型参考补偿控制器的具体应用中,为提高网络的映射能力和矢量控制系统的动态响应能力,本文在控制器中采用三层前向神经网络结构,并用广义PID神经网络自适应算法修正网络权值,采用这种算法在一定程度上加快了网络的收敛速度。
随后,在无速度传感器矢量控制系统中,本文探讨了基于神经网络PI控制器的磁链观测方法、定子电阻在线辨识方法和转速估算方法。
在转子磁场定向的无速度传感器矢量控制系统中,为保证矢量解耦控制的有效实现,需要进行精确的磁场定向。在众多磁通观测方法中,转子磁链的电压模型和电流模型各有优缺点且在很多方面具有互补性,因此很多方法利用电压-电流组合模型进行磁链观测。本文在静止坐标系下,探讨了一种基于神经网络PI控制器的电压-电流组合模型闭环磁通观测方法,通过电流模型对电压模型的自适应修正作用将二者结合起来完成转子磁链的观测过程,解决了电压模型观测器的不稳定问题。
在组合模型闭环磁链观测器中,随温度变化的定子电阻变化会直接影响转子磁链的观测效果,进而影响转速的控制精度,破坏系统的性能。为此,本文探讨了用神经网络PI控制器实现的定子电阻在线辨识方法,通过定子电阻在线辨识提高了转速控制精度。
在无速度传感器矢量控制系统的核心问题——转速估算环节中,为改进基于PI控制作用构造转速信号的系统的性能,本文探讨了一种基于神经网络PI控制器的转速估算方法。该方法将定参数PI调节器的PI参数直接作为神经网络权值,在输入偏差变化时,通过对权值PI参数的自适应调整,加快了系统的收敛速度、减小了超调量和稳定时间。
最后,本文在基于TMS320F2808的感应电机矢量控制系统实验平台上,进行了有速度传感器和无速度传感器矢量控制实验。在有速度传感器矢量控制实验中,采用神经网络PI速度控制器进行转速调节,证实了控制方案的有效性和可实现性。对于无速度传感器矢量控制,用估算转速进行闭环控制,验证了本文探讨的基于神经网络PI控制器的转速估算方法能有效地改善系统的动态性能,在突加负载扰动和低速运行时能保证系统稳定运行,同时验证了采用的逆变器死区电压补偿方法能有效改善电流波形,改进系统性能。
In recent years, induction motor has been extensively used in motion-control field with the advances in power devices, micro-processor and motor control theory. At the same time, its high performance is required increasingly. On one hand, as the performance of induction motor vector control system will be influenced by parameter variations, external load disturbances, etc., the high performance requirement can’t be met simply using traditional control methods. To make the system be robust to the influences, new control strategies have to be developed. On the other hand, the mounting of speed sensors brings some disadvantages to the induction motor vector control system in which rotor speed is closed-loop controlled. Therefore, the research to speed-sensorless drive system and enhancing its performance has become a popular research aspect in AC drive field. Considering the superior performance in the control and identification to nonlinear, complex and uncertain systems, neural networks was used in induction motor vector control system combined with PI control and model reference adaptive control in this dissertation, which has improved system performance.
At first, in order to solve the problem that satisfactory performance can’t be achieved using conventional PI controller when the system parameters vary in a wide range and reduce the influence of parameter variations and external load disturbances, the adaptive neural network PI speed controller and neural network model reference compensation controller were used in induction motor vector control system in the dissertation. The controller was constructed according to the characteristic of neural networks and vector control system and combining neural network with PI control and model reference adaptive control. Then, adaptive control to motor parameters and the load torque was realized.
In the application of the neural network model reference compensation controller, to enhance the mapping capability and dynamic response capability of the system, a three-layer forward neural network was used in the controller and adaptive control algorithm with generalized PID was used to correct the weights of the network. In a certain extent, the speed of convergence was accelerated by using the algorithm.
Subsequently, the flux observation method, stator resistance on-line identification method and speed estimation method based on the neural network PI controller were presented in the speed-sensorless vector control system.
In the rotor flux-oriented system, in order to guarantee the effective decoupling of vector control, the precise flux orientation is needed. Among many flux observation methods, voltage model and current model have their own advantages and disadvantages, and they are complementary in many ways, so they were combined in many flux observers. A closed-loop flux observation method using voltage-current combination model based on the neural network PI controller was presented in the dissertation, which realized the rotor flux observation and solved the instability of voltage model through the current model’s adaptive correction to the voltage model in stationary axes,.
In closed-loop flux observer using combination model, the stator resistance’s error by the temperature’s variation will impact on the accuracy of rotor flux observer and speed control, destroy the performance of the system. Therefore stator resistance on-line identification method based on neural network PI controller was presented, by which the accuracy of speed control was improved.
About the core of the speed-sensorless vector control system, that is speed estimation, in order to improve the performance of the system, in which the speed signal is structured with PI control, a speed estimation method based on the neural network PI controller was presented. In the method, the PI parameters were set as the weights of the neural network. When the input error varied, the weights were adaptively regulated, by which the convergence rate was accelerated, at the same time, the overshoot and stabilization time were reduced.
Finally, the experiments were done in the induction motor vector control system platform based on TMS320F2808. In the system with speed sensor, neural network PI controller was used for speed control. The experiment results verify the program is valid and feasible. In the system without speed sensor, the estimated speed is closed-loop controlled. The experiment results verify that system’s dynamic performance can be improved by using the speed estimation method based on neural network PI controller presented in the dissertation, the stability of the system was guaranteed in sudden increase in load disturbance and low-speed operation, the current waveform and system performance can be improved effectively by using the dead zone inverter voltage compensation method.
引文
[1] Fernando J. Von Zuben, Márcio L. A. Netto, Edson Bim et al. Adaptive vector control of a three-phase induction motor using neural networks. IEEE World Congress on Computational Intelligence, 1994, 6:3750 ~3755
[2] Guang Feng, Yan-Fei Liu, Lipei Huang. A new robust algorithm to improve the dynamic performance on the speed control of induction motor drive. IEEE Trans. On Power Electronics, 2004, 19(6):1614~1627
[3] Kuo-Kai Shyu, Faa-Jeng Lin, Hsin-Jeng Lin et al. Robust variable structure speed control for induction motor drive. IEEE Trans. On Aerospace and Electronic Systems, 1999, 35(1):215~224
[4] F. Rashidi, M. Rashidi. Robust sliding mode speed control with fuzzy approach for induction motors. IEEE International Conference on Industrial Technology, 2003, 1:27 ~30
[5] Ming-Fa Tsai, Ying-Yu Tzou. A transputer-based adaptive speed controller for AC induction motor drives with load torque estimation. IEEE Trans. On Industry Applications, 1997, 33(2):558~566
[6] Ying-Yu Tzou, Shore-Ting Yeh, Hua Wu. DSP-based rotor time constant identification and slip gain auto-tuning for indirect vector-controlled induction drives. IECON’96, 1996, 2:1228~1233
[7] Emanuele Cerruto, Alfio Consoli, Angelo Raciti et al. Fuzzy adaptive vector control of induction motor drives. IEEE Trans. On Power Electronics, 1997, 12(6):1028~1040
[8] H. A. F. Mohamed, W .P. Hew. A neural network vector control of induction motor. TENCON 2000, 2000, 3:336~340
[9]周有为,刘和平,刘述喜.感应电动机无速度传感器矢量控制综述.微电机,2006,39(4):57~60
[10]许大中.交流电机调速理论.杭州:浙江大学出版社,1991.1~41
[11]李永东.交流电机数字控制系统.北京:机械工业出版社,2003.1~305
[12]赵朝会,王永田,王新威等.现代交流调速技术的发展与现状.中州大学学报,2004,21(2):122~125
[13]李华德.交流调速控制系统.北京:电子工业出版社,2003.1~4
[14]吴守箴,臧英杰.电气传动的脉宽调制控制技术.北京:机械工业出版社,1995.4~9
[15]张崇巍,张兴. PWM整流器及其控制.北京:机械工业出版社,2003.226~227
[16] J. A. Santisteban, R. M. Stephan. Vector control methods for induction machines: an overview. IEEE Trans. On Education, 2001, 44(2):170~175
[17] Maurício Beltrǎo de Rossiter Corrěa, Cursino Brandǎo Jacobina, Edison Roberto Cabral da silva et al. Vector control strategies for single-phase induction motor drive systems. IEEE Trans. On Industrial Electronics, 2004, 51(5):1073~1080
[18] Sakae Yamamura. AC motors for high-performance applications: analysis and control. New York: Marcel Dekker, INC., 1986.1~9
[19] I.Takahashi, T.Noguchi. A new quick-response and high-efficiency control strategy of an induction motor. IEEE Trans. On Industry Applications, 1986, 22(5):820~827
[20] I.Takahashi,Y.ōhmori. High-performance direct torque control of an induction motor. IEEE Trans. On Industry Applications, 1989, 25(2):257~264
[21]李夙.异步电动机直接转矩控制.北京:机械工业出版社,1994.1~4
[22]蒋宗礼.人工神经网络导论.北京:高等教育出版社,2001.1~54
[23]何玉彬,李新忠.神经网络控制技术及其应用.北京:科学出版社,2000.1~97
[24]丛爽.神经网络、模糊系统及其在运动控制中的应用.合肥:中国科学技术大学出版社,2001.172~183
[25]徐中,孙伟,刘晓冰.单神经元自适应PID控制器的研究.大连理工大学学报,1999,39(5):667~672
[26]周箴,赵金,万淑芸.增量式单神经元控制器及其应用.华中理工大学学报,1998,1(增刊)
[27] G. Lightbody, G. W. Irwin. Direct neural model reference adaptive control. IEE Pro-D, 1995, 142(1):31~43
[28] Faa-Jeng Lin, Chih-Hong Lin, Po-Hung Shen. Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive. IEEE Trans. OnFuzzy Systems, 2001, 9(5):751~759
[29] K. Jalili, S. Farhangi, E. Saievar-Iranizad. Sensorless vector control of induction motors in fuel cell vehicle using a neuro-fuzzy speed controller and an online artificial neural network speed estimator. CCA '01, 2001:259~264
[30] T. Pajchrowski, K. Zawirski. Robust speed controller for PMSM based on artificial neural network. Power Electronics and Applications, 2005 European Conference, 2005:9
[31] T. N. N. El-Balluq, P. P. Acarnley, D. J. Atkinson. Adaptive tuning of a PI speed controller for a brushless DC motor: optimum speed control using a neural network. 2004 IEEE International Symposium on Industrial Electronics, 2004, 1:459~464
[32] F. F. M. El-Sousy. High-performance neural-network model-following speed controller for vector-controlled PMSM drive system. IEEE ICIT '04, 2004, 1:418~424
[33] Lee Yang Woo, Kim Dong, Kwak Il Doo. The design of motor speed controller using neural networks for disturbance reduction. IECON 2004, 2004, 2:1739~1742
[34] Xianglong Jiang, Jin Zhao, Hui Luo et al. Neural network speed controller of induction motor drive based on direct MRAC method. IECON '03, 2003, 3:2531~2536
[35] F. Kulic, D. Kukolj, E. Levi. Artificial neural network as a gain scheduler for PI speed controller in DC motor drives. Proceedings of the 5th Seminar on Neural Network Applications in Electrical Engineering, 2000:199~203
[36] F. M. El-Khouly, A. M. Sharaf, A. S. Abdel-Ghaffar et al. An adaptive neural network speed controller for permanent magnet DC motor drives. Proceedings of the 26th Southeastern Symposium on System Theory, 1994:416~420
[37]王永骥,涂健.神经元网络控制.北京:机械工业出版社,1998.1~345
[38] Seong-Hwan Kim, Tae-Sik Park, Ji-Yoon Yoo et al. Speed-Sensorless Vector control of an induction motor using neural network speed estimation. IEEE Trans. On Industrial Electronics, 2001, 48(3):609~614
[39] K. S. Narendra, K. Parthasarathy. Identification and control of dynamical systems using neural networks. IEEE Trans. On Neural Networks, 1990, 1(1):4~27
[40] M. G.. Simoes, B. K. Bose. Neural network based estimation of feedback signals for a vector controlled induction motor drive. IEEE Trans. On Industry Applications, 1995,31(3):620~629
[41] Michael T. Wishart, Ronald G. Harley. Identification and control of induction machines using artificial neural networks. IEEE Trans. On Industry Applications, 1995, 31(3):612~619
[42] D. Fodor, J. P. Six, D. Diana. Neural networks applied for induction motor speed sensorless estimation. ISIE’95, 1995, 1:181~186
[43] L. Ben-Brahim, R. Kurosawa. Identification of induction motor speed using neural networks. Power Conversion Conference, 1993:689~694
[44] B. Karanayil, M. F. Rahman, C. Grantham. On-line rotor resistance identification for induction motor drive with artificial neural networks supported by a simple PI stator resistance estimator. PEDS 2003, 2003, 1:433~438
[45] Chich-Yi Huang, Tien-Chi Chen, Ching-Lien Huang. Robust control of induction motor with a neural-network load torque estimator and a neural-network identification. IEEE Trans. On Industrial Electronics, 1999, 46(5):990~998
[46] Dazhi Wang, Renyuan Tang, Hui Jin et al. Sensorless-speed control strategy of induction motor based on artificial neural networks. WCICA 2004, 2004, 5:4467~4471
[47] Cheng-Zhi Cao, Mu-Ping Lu, Qi-Dong Zhang et al. Research on online identification of the stator resistance using wavelet neural network. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004, 5:3073~3077
[48] B. Karanayil, M. F. Rahman, C. Grantham. Investigation of an on-line rotor resistance identification with a new stator resistance observer for induction motor drive using artificial neural networks. PESC '03, 2003, 4:1883~1888
[49] F. A. Mohamed, H. Koivo. Modelling of induction motor using non-linear neural network system identification. SICE 2004 Annual Conference, 2004, 2:977~982
[50] B. Karanayil, M. F. Rahman, C. Grantham. Rotor resistance identification using artificial neural networks for a speed sensorless vector controlled induction motor drive. IECON '03, 2003, 1:419~ 424
[51] B. Karanayil, M. F. Rahman, C. Grantham. Stator and rotor resistance observers for induction motor drive using fuzzy logic and artificial neural networks. IEEE Trans. On Energy Conversion, 2005, 20(4):771~780
[52] B. Karanayil, M. F. Rahman, C. Grantham. Rotor resistance identification using artificial neural networks for an indirect vector controlled induction motor drive. IECON '01, 2001, 2:1315 ~1320
[53] A. Rubaai, R. Kotaru, M. D. Kankam. Parallel computation of continually on-line trained neural networks for identification and control of induction motors. Industry Applications Conference, Thirty-Fourth IAS Annual Meeting, 1999, 4:2364 ~2371
[54] L. Z. Martinez, A. Z. Martinez. Identification of induction machines using artificial neural networks. ISIE '97, 1997, 3:1259~1264
[55] Hong-Tzer Yang, Kuen-Yen Huang, Ching-Lien Huang. An ANN based identification and control approach for field-oriented induction motor. APSCOM-93, 1993, 2:744~750
[56] B. Burton, R. G. Harley, T.G. Habetler. Identification and control of induction motors using artificial neural networks with random weight change training. AFRICON, 1996, 2:833~836
[57] Baburaj Karanayil, Muhammed Fazlur Rahman, Colin Grantham. Online Stator and Rotor Resistance Estimation Scheme Using Artificial Neural Networks for Vector Controlled Speed Sensorless Induction Motor Drive. IEEE Trans. On Industrial Electronics, 2007, 54(1):167 ~ 176
[58] A. Zergaoui, A. Bennia. Identification and control of an asynchronous machine using neural networks. Proceedings of ICECS '99, 1999, 2:1043~1046
[59]陈伯时,杨耕.无速度传感器高性能交流调速控制的三条思路及其发展建议.电气传动,2006,36(1):3~8
[60]李永东,李明才.感应电机高性能无速度传感器控制系统——回顾、现状与展望.电气传动,2004,1:4~10
[61] C. Ilas, A. Bettini, L. Ferraris et al. Comparison of different schemes without shaft sensor for field oriented control drives. IECON’94, 1994, 3:1579~1588
[62] F. Z. Peng, T. Fukao. Robust speed identification for speed-sensorless vector control of induction motors. IEEE Trans. On Industry Applications, 1994, 30(5):1234~1240
[63] C. Schauder. Adaptive speed identification for vector control of induction motors without rotational transducers. IEEE Trans. On Industry Applications, 1992,28(5):1054~1061
[64] H. Kubota, K. Matsue, T. Nakano. DSP-based speed adaptive flux observer of induction motor. IEEE Trans. On Industry Applications, 1993, 29(2):344~348
[65] Geng Yang, Tung-hai Chin. Adaptive-speed identification scheme for a vector-controlled speed sensorless inverter-induction motor drive. IEEE Trans. On Industry Applications, 1993, 29(4):820~825
[66] K. L. Shi, T. F. Chan, Y. K. Wong et al. Speed estimation of an induction motor drive using an optimized extended Kalman filter. IEEE Trans. On Industrial Electronics, 2002, 49(1):124~133
[67] F. J. Lin, K. Shyu, R. J. Wai. DSP-based minmax speed sensorless induction motor drive with sliding mode model-following speed controller. IEE Proc.-Electr.Power Appl, 1999, 146(5):471~478
[68]王文森.基于PI自适应法的无速度传感器异步电动机矢量控制系统.电工技术学报,2002,17(1):1~6
[69]宫明玉,廖晓钟,冬雷.无速度传感器异步电机按定子磁链定向的矢量控制系统.电气传动,2005,35(7):20~23
[70] B. K. Bose, M. G. Simoes. Speed sensorless hybrid vector controlled induction motor drive. Conf Record of IEEE-IAS Annual Meeting, 1995:137~143
[71] T. Ohtani, N. Takada, K.Tanaka. Vector control of induction motor without shaft encoder. IEEE Trans. On Industry Applications, 1992, 28(1):157~164
[72]邹旭东,康勇,陈坚.感应电机矢量控制系统无速度传感器控制方案研究.电气传动,2004,4:3~7
[73] T. D. Batzel, K. Y. Lee. An approach to sensorless operation of the permanent-magnet synchronous motor using diagonally recurrent neural networks. IEEE Trans. On Energy Conversion, 2003, 18(1):100~106
[74]程善美,姜向龙,闵松等. PI-神经网络混合速度控制器的研究.电气传动,2004,1:23~25
[75] Toshio Fukuda, Takanori Shibata. Theory and application of neural networks for industrial control systems. IEEE Trans. On Industrial Electronics, 1992, 39(6):472~489
[76] Yao Zhang, P. Sen, G. E. Hearn. An on-line trained adaptive neural controller. IEEE Control Systems Magazine, 1995, 15(5):67~75
[77] Chao-Chee Ku, Kwang Y Lee. Diagonal Recurrent neural networks for dynamic systems control. IEEE Trans. On Neural Networks, 1995, 6(1):144~156
[78] Tien-Chi Chen, Tsong-Terng Sheu. Model reference neural network controller for induction motor speed control. IEEE Trans. On Energy Conversion, 2002, 17(2):157~162
[79] Tien-Chi Chen, Tsong-Terng Sheu. Model reference robust speed control for induction-motor drive with time delay based on neural network. IEEE Trans. On Systems, Man, and Cybernetics-Part A: Systems and Humans, 2001, 31(6):746~753
[80] Kuo-Kai Shyu, Hsin-Jang Shieh, Sheng-Shang Fu. Model reference adaptive speed control for induction motor drive using neural networks. IEEE Trans. On Industrial Electronics, 1998, 45(1):180~182
[81]朗道.自适应控制――模型参考方法.北京:国防工业出版社,1985.1~22
[82]赵林明,胡浩云,魏德华等.多层前向人工神经网络.河南:黄河水利出版社,1999.1~37
[83] Simon Haykin. Neural networks: a comprehensive foundation. New York: Macmillan College Publishing Company, 1994.181~185
[84] Faa-Jeng Lin, Rong-Jong, Chun-Ming Hong. Identification and control of rotary traveling-wave type ultrasonic motor using neural networks. IEEE Trans. On Control Systems Technology, 2001, 9(4):672~680
[85] P. L. Jansen, R. D. Lorenz. A physically insightful approach to the design and accuracy assessment of flux observers for field oriented induction machine drives. IEEE Trans. On Industry Applications, 1994, 30(1):101~110
[86] M. Elbuluk, N. Langovsky, M. D. Kankam. Design and implementation of a closed-loop observer and adaptive controller for induction motor drives. IEEE Trans. On Industry Applications, 1998, 34(3):435~443
[87] H. Madadi Kojabadi, L. Chang. Model reference adaptive system pseudoreduced-order flux observer for very low speed and zero speed estimation in sensorless induction motor drives. Power Electronics Specialists Conference, 2002,1:301~305
[88] M. Hinkkanen, J. Luomi. Parameter sensitivity of full-order flux observers for induction motors. IEEE Trans. On Industry Applications, 2003, 39(4):1127~1135
[89] M. Hinkkanen. Analysis and design of full-order flux observers for sensorless induction motors. IEEE Trans. On Industrial Electronics, 2004, 51(5):1033~1040
[90] H. M. Kojabadi, Liuchen Chang, R. Doraiswami. A MRAS-based adaptive pseudoreduced-order flux observer for sensorless induction motor drives. IEEE Trans. On Power Electronics, 2005, 20(4):930~938
[91] Jingchuan Li, Longya Xu, Zheng Zhang. An adaptive sliding-mode observer for induction motor sensorless speed control. IEEE Trans. On Industry Applications, 2005, 41(4):1039~1046
[92] C. Lascu, G. D. Andresscu. Sliding-mode observer and improved integrator with DC-offset compensation for flux estimation in sensorless-controlled induction motors. IEEE Trans. On Industrial Electronics, 2006, 53(3):785~794
[93]陈硕.基于MRAS理论的无速度传感器矢量控制调速系统.福州大学学报(自然科学版),2001,29(2):42~45
[94]陈硕,辻峰男.基于磁通观察器的感应电机无速度传感器矢量控制系统.电工技术学报,2001,16(4):30~33
[95]陈硕,辻峰男,山田英二.感应电机无速度传感器矢量控制系统的定子电阻在线辨识.中国电机工程学报,2003,23(2):88~92
[96] Kan Akatsu, Atauo Kawamura. Sensorless very low-speed and zero-speed estimations with online rotor resistance estimation of induction motor without additional signal injection. IEEE Trans. On Industry Applications, 2000, 36(3):764~771
[97] F. J. Lin, R. J. Wai, P. C. Lin. Robust speed sensorless induction motor drive. IEEE Trans. On Industrial Electronics, 1999, 35(2):566~578
[98]陈硕,吴臻鹏,阮成功.基于变参数PI自适应法的无速度传感器矢量控制系统速度推算方法.福州大学学报(自然科学版),2003,31(1):65~68
[99]王晓明,王玲.电动机的DSP控制——TI公司DSP应用.北京:北京航空航天大学出版社,2004.1~167
[100] Namho Hur, Kichul Hong, Kwanghee Nam. Sensorless vector control in thepresence of voltage and current measurement errors by dead-time. Industry Applications Conference, 1997, 1:433~438
[101] T. Sukegawa, K. Kamiyama, K. Mizuno et al. Fully digital, vector-controlled PWM VSI-fed AC drives with an inverter dead-time compensation strategy. IEEE Trans. On Industry Applications, 1991, 27(3):552~559
[102] S. H. Kim, T. S. Park, J.Y. Yoo et al. Dead time compensation in a vector-controlled induction machine. Power Electronics Specialists Conference, 1998, 2:1011~1016
[2] Guang Feng, Yan-Fei Liu, Lipei Huang. A new robust algorithm to improve the dynamic performance on the speed control of induction motor drive. IEEE Trans. On Power Electronics, 2004, 19(6):1614~1627
[3] Kuo-Kai Shyu, Faa-Jeng Lin, Hsin-Jeng Lin et al. Robust variable structure speed control for induction motor drive. IEEE Trans. On Aerospace and Electronic Systems, 1999, 35(1):215~224
[4] F. Rashidi, M. Rashidi. Robust sliding mode speed control with fuzzy approach for induction motors. IEEE International Conference on Industrial Technology, 2003, 1:27 ~30
[5] Ming-Fa Tsai, Ying-Yu Tzou. A transputer-based adaptive speed controller for AC induction motor drives with load torque estimation. IEEE Trans. On Industry Applications, 1997, 33(2):558~566
[6] Ying-Yu Tzou, Shore-Ting Yeh, Hua Wu. DSP-based rotor time constant identification and slip gain auto-tuning for indirect vector-controlled induction drives. IECON’96, 1996, 2:1228~1233
[7] Emanuele Cerruto, Alfio Consoli, Angelo Raciti et al. Fuzzy adaptive vector control of induction motor drives. IEEE Trans. On Power Electronics, 1997, 12(6):1028~1040
[8] H. A. F. Mohamed, W .P. Hew. A neural network vector control of induction motor. TENCON 2000, 2000, 3:336~340
[9]周有为,刘和平,刘述喜.感应电动机无速度传感器矢量控制综述.微电机,2006,39(4):57~60
[10]许大中.交流电机调速理论.杭州:浙江大学出版社,1991.1~41
[11]李永东.交流电机数字控制系统.北京:机械工业出版社,2003.1~305
[12]赵朝会,王永田,王新威等.现代交流调速技术的发展与现状.中州大学学报,2004,21(2):122~125
[13]李华德.交流调速控制系统.北京:电子工业出版社,2003.1~4
[14]吴守箴,臧英杰.电气传动的脉宽调制控制技术.北京:机械工业出版社,1995.4~9
[15]张崇巍,张兴. PWM整流器及其控制.北京:机械工业出版社,2003.226~227
[16] J. A. Santisteban, R. M. Stephan. Vector control methods for induction machines: an overview. IEEE Trans. On Education, 2001, 44(2):170~175
[17] Maurício Beltrǎo de Rossiter Corrěa, Cursino Brandǎo Jacobina, Edison Roberto Cabral da silva et al. Vector control strategies for single-phase induction motor drive systems. IEEE Trans. On Industrial Electronics, 2004, 51(5):1073~1080
[18] Sakae Yamamura. AC motors for high-performance applications: analysis and control. New York: Marcel Dekker, INC., 1986.1~9
[19] I.Takahashi, T.Noguchi. A new quick-response and high-efficiency control strategy of an induction motor. IEEE Trans. On Industry Applications, 1986, 22(5):820~827
[20] I.Takahashi,Y.ōhmori. High-performance direct torque control of an induction motor. IEEE Trans. On Industry Applications, 1989, 25(2):257~264
[21]李夙.异步电动机直接转矩控制.北京:机械工业出版社,1994.1~4
[22]蒋宗礼.人工神经网络导论.北京:高等教育出版社,2001.1~54
[23]何玉彬,李新忠.神经网络控制技术及其应用.北京:科学出版社,2000.1~97
[24]丛爽.神经网络、模糊系统及其在运动控制中的应用.合肥:中国科学技术大学出版社,2001.172~183
[25]徐中,孙伟,刘晓冰.单神经元自适应PID控制器的研究.大连理工大学学报,1999,39(5):667~672
[26]周箴,赵金,万淑芸.增量式单神经元控制器及其应用.华中理工大学学报,1998,1(增刊)
[27] G. Lightbody, G. W. Irwin. Direct neural model reference adaptive control. IEE Pro-D, 1995, 142(1):31~43
[28] Faa-Jeng Lin, Chih-Hong Lin, Po-Hung Shen. Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive. IEEE Trans. OnFuzzy Systems, 2001, 9(5):751~759
[29] K. Jalili, S. Farhangi, E. Saievar-Iranizad. Sensorless vector control of induction motors in fuel cell vehicle using a neuro-fuzzy speed controller and an online artificial neural network speed estimator. CCA '01, 2001:259~264
[30] T. Pajchrowski, K. Zawirski. Robust speed controller for PMSM based on artificial neural network. Power Electronics and Applications, 2005 European Conference, 2005:9
[31] T. N. N. El-Balluq, P. P. Acarnley, D. J. Atkinson. Adaptive tuning of a PI speed controller for a brushless DC motor: optimum speed control using a neural network. 2004 IEEE International Symposium on Industrial Electronics, 2004, 1:459~464
[32] F. F. M. El-Sousy. High-performance neural-network model-following speed controller for vector-controlled PMSM drive system. IEEE ICIT '04, 2004, 1:418~424
[33] Lee Yang Woo, Kim Dong, Kwak Il Doo. The design of motor speed controller using neural networks for disturbance reduction. IECON 2004, 2004, 2:1739~1742
[34] Xianglong Jiang, Jin Zhao, Hui Luo et al. Neural network speed controller of induction motor drive based on direct MRAC method. IECON '03, 2003, 3:2531~2536
[35] F. Kulic, D. Kukolj, E. Levi. Artificial neural network as a gain scheduler for PI speed controller in DC motor drives. Proceedings of the 5th Seminar on Neural Network Applications in Electrical Engineering, 2000:199~203
[36] F. M. El-Khouly, A. M. Sharaf, A. S. Abdel-Ghaffar et al. An adaptive neural network speed controller for permanent magnet DC motor drives. Proceedings of the 26th Southeastern Symposium on System Theory, 1994:416~420
[37]王永骥,涂健.神经元网络控制.北京:机械工业出版社,1998.1~345
[38] Seong-Hwan Kim, Tae-Sik Park, Ji-Yoon Yoo et al. Speed-Sensorless Vector control of an induction motor using neural network speed estimation. IEEE Trans. On Industrial Electronics, 2001, 48(3):609~614
[39] K. S. Narendra, K. Parthasarathy. Identification and control of dynamical systems using neural networks. IEEE Trans. On Neural Networks, 1990, 1(1):4~27
[40] M. G.. Simoes, B. K. Bose. Neural network based estimation of feedback signals for a vector controlled induction motor drive. IEEE Trans. On Industry Applications, 1995,31(3):620~629
[41] Michael T. Wishart, Ronald G. Harley. Identification and control of induction machines using artificial neural networks. IEEE Trans. On Industry Applications, 1995, 31(3):612~619
[42] D. Fodor, J. P. Six, D. Diana. Neural networks applied for induction motor speed sensorless estimation. ISIE’95, 1995, 1:181~186
[43] L. Ben-Brahim, R. Kurosawa. Identification of induction motor speed using neural networks. Power Conversion Conference, 1993:689~694
[44] B. Karanayil, M. F. Rahman, C. Grantham. On-line rotor resistance identification for induction motor drive with artificial neural networks supported by a simple PI stator resistance estimator. PEDS 2003, 2003, 1:433~438
[45] Chich-Yi Huang, Tien-Chi Chen, Ching-Lien Huang. Robust control of induction motor with a neural-network load torque estimator and a neural-network identification. IEEE Trans. On Industrial Electronics, 1999, 46(5):990~998
[46] Dazhi Wang, Renyuan Tang, Hui Jin et al. Sensorless-speed control strategy of induction motor based on artificial neural networks. WCICA 2004, 2004, 5:4467~4471
[47] Cheng-Zhi Cao, Mu-Ping Lu, Qi-Dong Zhang et al. Research on online identification of the stator resistance using wavelet neural network. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004, 5:3073~3077
[48] B. Karanayil, M. F. Rahman, C. Grantham. Investigation of an on-line rotor resistance identification with a new stator resistance observer for induction motor drive using artificial neural networks. PESC '03, 2003, 4:1883~1888
[49] F. A. Mohamed, H. Koivo. Modelling of induction motor using non-linear neural network system identification. SICE 2004 Annual Conference, 2004, 2:977~982
[50] B. Karanayil, M. F. Rahman, C. Grantham. Rotor resistance identification using artificial neural networks for a speed sensorless vector controlled induction motor drive. IECON '03, 2003, 1:419~ 424
[51] B. Karanayil, M. F. Rahman, C. Grantham. Stator and rotor resistance observers for induction motor drive using fuzzy logic and artificial neural networks. IEEE Trans. On Energy Conversion, 2005, 20(4):771~780
[52] B. Karanayil, M. F. Rahman, C. Grantham. Rotor resistance identification using artificial neural networks for an indirect vector controlled induction motor drive. IECON '01, 2001, 2:1315 ~1320
[53] A. Rubaai, R. Kotaru, M. D. Kankam. Parallel computation of continually on-line trained neural networks for identification and control of induction motors. Industry Applications Conference, Thirty-Fourth IAS Annual Meeting, 1999, 4:2364 ~2371
[54] L. Z. Martinez, A. Z. Martinez. Identification of induction machines using artificial neural networks. ISIE '97, 1997, 3:1259~1264
[55] Hong-Tzer Yang, Kuen-Yen Huang, Ching-Lien Huang. An ANN based identification and control approach for field-oriented induction motor. APSCOM-93, 1993, 2:744~750
[56] B. Burton, R. G. Harley, T.G. Habetler. Identification and control of induction motors using artificial neural networks with random weight change training. AFRICON, 1996, 2:833~836
[57] Baburaj Karanayil, Muhammed Fazlur Rahman, Colin Grantham. Online Stator and Rotor Resistance Estimation Scheme Using Artificial Neural Networks for Vector Controlled Speed Sensorless Induction Motor Drive. IEEE Trans. On Industrial Electronics, 2007, 54(1):167 ~ 176
[58] A. Zergaoui, A. Bennia. Identification and control of an asynchronous machine using neural networks. Proceedings of ICECS '99, 1999, 2:1043~1046
[59]陈伯时,杨耕.无速度传感器高性能交流调速控制的三条思路及其发展建议.电气传动,2006,36(1):3~8
[60]李永东,李明才.感应电机高性能无速度传感器控制系统——回顾、现状与展望.电气传动,2004,1:4~10
[61] C. Ilas, A. Bettini, L. Ferraris et al. Comparison of different schemes without shaft sensor for field oriented control drives. IECON’94, 1994, 3:1579~1588
[62] F. Z. Peng, T. Fukao. Robust speed identification for speed-sensorless vector control of induction motors. IEEE Trans. On Industry Applications, 1994, 30(5):1234~1240
[63] C. Schauder. Adaptive speed identification for vector control of induction motors without rotational transducers. IEEE Trans. On Industry Applications, 1992,28(5):1054~1061
[64] H. Kubota, K. Matsue, T. Nakano. DSP-based speed adaptive flux observer of induction motor. IEEE Trans. On Industry Applications, 1993, 29(2):344~348
[65] Geng Yang, Tung-hai Chin. Adaptive-speed identification scheme for a vector-controlled speed sensorless inverter-induction motor drive. IEEE Trans. On Industry Applications, 1993, 29(4):820~825
[66] K. L. Shi, T. F. Chan, Y. K. Wong et al. Speed estimation of an induction motor drive using an optimized extended Kalman filter. IEEE Trans. On Industrial Electronics, 2002, 49(1):124~133
[67] F. J. Lin, K. Shyu, R. J. Wai. DSP-based minmax speed sensorless induction motor drive with sliding mode model-following speed controller. IEE Proc.-Electr.Power Appl, 1999, 146(5):471~478
[68]王文森.基于PI自适应法的无速度传感器异步电动机矢量控制系统.电工技术学报,2002,17(1):1~6
[69]宫明玉,廖晓钟,冬雷.无速度传感器异步电机按定子磁链定向的矢量控制系统.电气传动,2005,35(7):20~23
[70] B. K. Bose, M. G. Simoes. Speed sensorless hybrid vector controlled induction motor drive. Conf Record of IEEE-IAS Annual Meeting, 1995:137~143
[71] T. Ohtani, N. Takada, K.Tanaka. Vector control of induction motor without shaft encoder. IEEE Trans. On Industry Applications, 1992, 28(1):157~164
[72]邹旭东,康勇,陈坚.感应电机矢量控制系统无速度传感器控制方案研究.电气传动,2004,4:3~7
[73] T. D. Batzel, K. Y. Lee. An approach to sensorless operation of the permanent-magnet synchronous motor using diagonally recurrent neural networks. IEEE Trans. On Energy Conversion, 2003, 18(1):100~106
[74]程善美,姜向龙,闵松等. PI-神经网络混合速度控制器的研究.电气传动,2004,1:23~25
[75] Toshio Fukuda, Takanori Shibata. Theory and application of neural networks for industrial control systems. IEEE Trans. On Industrial Electronics, 1992, 39(6):472~489
[76] Yao Zhang, P. Sen, G. E. Hearn. An on-line trained adaptive neural controller. IEEE Control Systems Magazine, 1995, 15(5):67~75
[77] Chao-Chee Ku, Kwang Y Lee. Diagonal Recurrent neural networks for dynamic systems control. IEEE Trans. On Neural Networks, 1995, 6(1):144~156
[78] Tien-Chi Chen, Tsong-Terng Sheu. Model reference neural network controller for induction motor speed control. IEEE Trans. On Energy Conversion, 2002, 17(2):157~162
[79] Tien-Chi Chen, Tsong-Terng Sheu. Model reference robust speed control for induction-motor drive with time delay based on neural network. IEEE Trans. On Systems, Man, and Cybernetics-Part A: Systems and Humans, 2001, 31(6):746~753
[80] Kuo-Kai Shyu, Hsin-Jang Shieh, Sheng-Shang Fu. Model reference adaptive speed control for induction motor drive using neural networks. IEEE Trans. On Industrial Electronics, 1998, 45(1):180~182
[81]朗道.自适应控制――模型参考方法.北京:国防工业出版社,1985.1~22
[82]赵林明,胡浩云,魏德华等.多层前向人工神经网络.河南:黄河水利出版社,1999.1~37
[83] Simon Haykin. Neural networks: a comprehensive foundation. New York: Macmillan College Publishing Company, 1994.181~185
[84] Faa-Jeng Lin, Rong-Jong, Chun-Ming Hong. Identification and control of rotary traveling-wave type ultrasonic motor using neural networks. IEEE Trans. On Control Systems Technology, 2001, 9(4):672~680
[85] P. L. Jansen, R. D. Lorenz. A physically insightful approach to the design and accuracy assessment of flux observers for field oriented induction machine drives. IEEE Trans. On Industry Applications, 1994, 30(1):101~110
[86] M. Elbuluk, N. Langovsky, M. D. Kankam. Design and implementation of a closed-loop observer and adaptive controller for induction motor drives. IEEE Trans. On Industry Applications, 1998, 34(3):435~443
[87] H. Madadi Kojabadi, L. Chang. Model reference adaptive system pseudoreduced-order flux observer for very low speed and zero speed estimation in sensorless induction motor drives. Power Electronics Specialists Conference, 2002,1:301~305
[88] M. Hinkkanen, J. Luomi. Parameter sensitivity of full-order flux observers for induction motors. IEEE Trans. On Industry Applications, 2003, 39(4):1127~1135
[89] M. Hinkkanen. Analysis and design of full-order flux observers for sensorless induction motors. IEEE Trans. On Industrial Electronics, 2004, 51(5):1033~1040
[90] H. M. Kojabadi, Liuchen Chang, R. Doraiswami. A MRAS-based adaptive pseudoreduced-order flux observer for sensorless induction motor drives. IEEE Trans. On Power Electronics, 2005, 20(4):930~938
[91] Jingchuan Li, Longya Xu, Zheng Zhang. An adaptive sliding-mode observer for induction motor sensorless speed control. IEEE Trans. On Industry Applications, 2005, 41(4):1039~1046
[92] C. Lascu, G. D. Andresscu. Sliding-mode observer and improved integrator with DC-offset compensation for flux estimation in sensorless-controlled induction motors. IEEE Trans. On Industrial Electronics, 2006, 53(3):785~794
[93]陈硕.基于MRAS理论的无速度传感器矢量控制调速系统.福州大学学报(自然科学版),2001,29(2):42~45
[94]陈硕,辻峰男.基于磁通观察器的感应电机无速度传感器矢量控制系统.电工技术学报,2001,16(4):30~33
[95]陈硕,辻峰男,山田英二.感应电机无速度传感器矢量控制系统的定子电阻在线辨识.中国电机工程学报,2003,23(2):88~92
[96] Kan Akatsu, Atauo Kawamura. Sensorless very low-speed and zero-speed estimations with online rotor resistance estimation of induction motor without additional signal injection. IEEE Trans. On Industry Applications, 2000, 36(3):764~771
[97] F. J. Lin, R. J. Wai, P. C. Lin. Robust speed sensorless induction motor drive. IEEE Trans. On Industrial Electronics, 1999, 35(2):566~578
[98]陈硕,吴臻鹏,阮成功.基于变参数PI自适应法的无速度传感器矢量控制系统速度推算方法.福州大学学报(自然科学版),2003,31(1):65~68
[99]王晓明,王玲.电动机的DSP控制——TI公司DSP应用.北京:北京航空航天大学出版社,2004.1~167
[100] Namho Hur, Kichul Hong, Kwanghee Nam. Sensorless vector control in thepresence of voltage and current measurement errors by dead-time. Industry Applications Conference, 1997, 1:433~438
[101] T. Sukegawa, K. Kamiyama, K. Mizuno et al. Fully digital, vector-controlled PWM VSI-fed AC drives with an inverter dead-time compensation strategy. IEEE Trans. On Industry Applications, 1991, 27(3):552~559
[102] S. H. Kim, T. S. Park, J.Y. Yoo et al. Dead time compensation in a vector-controlled induction machine. Power Electronics Specialists Conference, 1998, 2:1011~1016