永磁同步电机的参数辨识及控制策略研究
|
摘要
摘要：交流永磁同步电机以其结构简单、运行可靠、体积小、损耗低、效率高等特点,在数控机床、电动汽车、机器人等方面获得了广泛应用。论文在对交流永磁同步电机的原理、数学模型和空间矢量脉宽调制(SVPWM)技术进行研究的基础上,对永磁同步电机的参数测试、辨识进行了大量的理论和实验研究,同时对永磁同步电机基于自抗扰和灰色理论的控制策略也进行了深入研究。仿真和实验结果验证了新的参数测试、辨识方法和自抗扰控制的有效性。
论文对永磁同步电机的几个主要参数的测试方法进行了介绍。针对采用电压积分法测量交、直轴电感Lq,Ld中必须使用磁通计这一专用设备的要求,考虑到目前实验室和工作现场的实际情况,提出了一种替代解决方案,即：利用通用的电压采集装置(如：示波器)进行数据采集,然后采用数值积分法对采集到的离散电压信号进行积分,得到永磁电机的磁链参数,进而计算出永磁电机的电感参数。该方法具有简便、易于实现的特点。实验结果验证了该方法的有效性。
针对永磁同步电机的绕组电阻Rs和交、直轴电感L。,Ld会随温度发生较大变化的问题,通过数学方法和仿真研究了这些参数随温度变化的辨识问题。论文在MATLAB中构造了一种参数能在线变化的永磁同步电机仿真模型,采用扩展卡尔曼(EKM)滤波和Elman神经网络,递推最小二乘方法进行了辨识。这两种辨识方法虽然也能达到较高的辨识精度,但由于存在计算中数据量大,难以实现实时辨识的缺点,提出了一种加窗最小二乘方法。该方法可根据实际要求确定所需的数据长度,计算量小,既保证了辨识精度又提高了实时性,还解决了递推最小二乘方法存在的数据饱和问题。这三种辨识方法的仿真结果比较表明：加窗最小二乘法具有最优的辨识效果。
为进一步提高永磁同步电机工作的稳定性和控制性能,论文首先采用自抗扰控制技术对电源、转矩等扰动了有效抑制,仿真结果表明自抗扰的控制性能优于PID控制；论文在自抗扰控制的基础上,将最优控制、神经网络与自抗扰技术结合,提出了永磁同步电机自寻最优自抗扰控制和基于神经网络的自抗扰控制算法。永磁同步电机自寻最优自抗扰控制除了具有比自抗扰更高的控制性能以外,还具有控制能量最少的特点。为消除永磁同步电机伺服系统运行过程中存在的外部负载等扰动项的变化对调速性能的影响,论文将神经网络嵌入到自抗扰控制器中,利用神经网络能任意逼近非线性函数的能力去补偿对象的一部分变化,这样既可近似地使原系统参数变化范围变小,还能提高自抗扰控制的状态估计能力,从而减轻ESO的负担,有利于提高控制器的运算和响应速度。研究结果表明这两种方法可以提供比自抗扰更优的控制效果。
为实现永磁同步伺服系统的参数在线辨识与控制,通过自行搭建的永磁同步电机模型,模拟温度从20℃-80℃时绕组电阻Rs和交、直轴电感Lq,Ld的变化情况,采用加窗最小二乘法实时辨识参数,转速环采用自抗扰控制。仿真结果显示：采用参数在线辨识的自抗扰控制系统性能比单纯采用PID的系统性能更优。
为了利用“少数据”来实现永磁同步电机的控制,论文首次将灰色补偿PID和灰色预测PID控制算法同时应用到永磁同步电机伺服系统中,仿真结果表明：与PID控制相比,该控制策略对各种干扰具有更好的鲁棒性和适应性。
自行研发了一套交流永磁同步电机伺服系统,实现了基于三闭环的SVPWM控制。伺服系统的空载和负载试验表明,与经典PID控制相比,自抗扰控制具有响应快、无超调、抗扰动能力强等特点。
此外,针对IGBT驱动中存在现场参数变化问题,专门开发了一种可对IGBT开通和关断时间进行调节、具有驱动电源欠压保护功能的新型驱动电路,取得了良好的应用效果。
ABSTRACT: Due to its uncomplicated structure, excellent control performance, small size, low loss and high efficiency, AC PMSM (Alternating Current Permanent Magnet Synchronous Motor) is widely used in the fields of Numerically-controlled machine tool, electric automobile, and robot, etc. Based on the research for its principle of work, its mathematical model and SVPWM (Space Vector Pulse Width Modulation), the author does a lot of theoretical and practical research for the tests as well as identification of PMSM's parameters. Besides, control strategies of PMSM based on the theory of ADRC (active disturbance rejection control) and Gray Theory are also studied deeply. Simulation and experiment results demonstrate the effectiveness of new ways to test and identify parameters, as well as ADRC.
A new method to test the several main parameters of PMSM is proposed in this paper. Since the magnet meter cannot be avoided in Voltage Integration method which is used to get Ld (direct-axis inductance) and Lq (quadrature-axis inductance) and considering the conditions of lab and worksite, this paper proposes an alternate method. Steps of this method are as follows. To begin with, data should be gathered by general devices of voltage collecting, such as oscilloscope. Then the discrete voltage signal can be integrated through Numerical-integration method to get PMSM's flux linkage which can be used to compute its inductance. This method is simple and easy to implement. Experiment results have proved the effectiveness of this new method.
Since Rs (wingding resistance), Ld and Lq will vary a lot with temperature, identification of these parameters are studied though mathematical analysis and simulation. The author has built a simulation model of PMSM in MATLAB whose parameters can vary online, and identified the parameters using EKF (Extended Kalman) filter & Elman neural network and RLS (Recursive Least Square) method. Despite that both of the two methods can guarantee high identification accuracy, they are unsuitable to identify online and have big compute loads. Based on the above disadvantages, WLS (Windowed Least Square) method is proposed. In this method, the length of the window can be adjusted according to practical conditions. Besides, it has a small computational burden. That is why it can guarantee the identification accuracy and excellent timeliness. And at the same time, the issue of data saturation of RLS can be avoided. Compared with simulation results of the other two methods, WLS's results show that this method is the most effective.
To improve the working stability and control performance of PMSM, ADRC is applied to reject disturbances from power supply and torque. Simulation results prove that the control performance of ADRC is better than PID control. Then, based on the ADRC theory, the author combines Optimal control, Neural networks and ADRC method together and proposes two algorithms named Self-optimizing ADRC method and ADRC based on neural networks, respectively. Compared with ADRC method, the Self-optimizing ADRC method has better control performance and least control energy. To eliminate the influence of varying disturbance like external load on the control performance of rotation speed, neural network is bedded in ADRC, which can approximately narrow the vary range of original system's parameters, and at the same time improve ADRC's ability of state estimation so that it is possible for ESO (Extended State Observer) to work with lighter load. What's more, by doing this, controller's computing and response speed will be improved. It is proved by study results that both of the methods can provide better control effect.
In order to identify and control parameters of PMSM online, the author builds a control model of PMSM and simulates change process of Rs, Ld and Lq with temperature varying from 20℃to 80℃. Then the parameters are identified online using WLS method while ADRC is applied in speed loop. Simulation results demonstrate that the performance of ADRC system using online estimation strategy is better than that of the system using only PID method.
In this paper, Gray Compensation PID algorithm and Gray Predict PID Control algorithm are applied in PMSM Servo System for the first time to control PMSM with less data, and results show that this control strategy has more satisfied robustness and adaptive capacity.
A set of AC PMSM Servo System has been developed to realize the SVPWM control based on three closed loop. The load test and no-load test prove that ADRC has the advantages of fast response, no over modulation, and strong ability of rejecting disturbance, compared with classical PID control.
In addition, since parameters of IGBT will change in work site, the author has developed a new kind of circuit which can modulate the turn-on and turn-off time of IGBT. Moreover, this driving circuit is also able to protect itself when power supply is under-voltage. And this kind of circuit is proved to be effective.
论文对永磁同步电机的几个主要参数的测试方法进行了介绍。针对采用电压积分法测量交、直轴电感Lq,Ld中必须使用磁通计这一专用设备的要求,考虑到目前实验室和工作现场的实际情况,提出了一种替代解决方案,即：利用通用的电压采集装置(如：示波器)进行数据采集,然后采用数值积分法对采集到的离散电压信号进行积分,得到永磁电机的磁链参数,进而计算出永磁电机的电感参数。该方法具有简便、易于实现的特点。实验结果验证了该方法的有效性。
针对永磁同步电机的绕组电阻Rs和交、直轴电感L。,Ld会随温度发生较大变化的问题,通过数学方法和仿真研究了这些参数随温度变化的辨识问题。论文在MATLAB中构造了一种参数能在线变化的永磁同步电机仿真模型,采用扩展卡尔曼(EKM)滤波和Elman神经网络,递推最小二乘方法进行了辨识。这两种辨识方法虽然也能达到较高的辨识精度,但由于存在计算中数据量大,难以实现实时辨识的缺点,提出了一种加窗最小二乘方法。该方法可根据实际要求确定所需的数据长度,计算量小,既保证了辨识精度又提高了实时性,还解决了递推最小二乘方法存在的数据饱和问题。这三种辨识方法的仿真结果比较表明：加窗最小二乘法具有最优的辨识效果。
为进一步提高永磁同步电机工作的稳定性和控制性能,论文首先采用自抗扰控制技术对电源、转矩等扰动了有效抑制,仿真结果表明自抗扰的控制性能优于PID控制；论文在自抗扰控制的基础上,将最优控制、神经网络与自抗扰技术结合,提出了永磁同步电机自寻最优自抗扰控制和基于神经网络的自抗扰控制算法。永磁同步电机自寻最优自抗扰控制除了具有比自抗扰更高的控制性能以外,还具有控制能量最少的特点。为消除永磁同步电机伺服系统运行过程中存在的外部负载等扰动项的变化对调速性能的影响,论文将神经网络嵌入到自抗扰控制器中,利用神经网络能任意逼近非线性函数的能力去补偿对象的一部分变化,这样既可近似地使原系统参数变化范围变小,还能提高自抗扰控制的状态估计能力,从而减轻ESO的负担,有利于提高控制器的运算和响应速度。研究结果表明这两种方法可以提供比自抗扰更优的控制效果。
为实现永磁同步伺服系统的参数在线辨识与控制,通过自行搭建的永磁同步电机模型,模拟温度从20℃-80℃时绕组电阻Rs和交、直轴电感Lq,Ld的变化情况,采用加窗最小二乘法实时辨识参数,转速环采用自抗扰控制。仿真结果显示：采用参数在线辨识的自抗扰控制系统性能比单纯采用PID的系统性能更优。
为了利用“少数据”来实现永磁同步电机的控制,论文首次将灰色补偿PID和灰色预测PID控制算法同时应用到永磁同步电机伺服系统中,仿真结果表明：与PID控制相比,该控制策略对各种干扰具有更好的鲁棒性和适应性。
自行研发了一套交流永磁同步电机伺服系统,实现了基于三闭环的SVPWM控制。伺服系统的空载和负载试验表明,与经典PID控制相比,自抗扰控制具有响应快、无超调、抗扰动能力强等特点。
此外,针对IGBT驱动中存在现场参数变化问题,专门开发了一种可对IGBT开通和关断时间进行调节、具有驱动电源欠压保护功能的新型驱动电路,取得了良好的应用效果。
ABSTRACT: Due to its uncomplicated structure, excellent control performance, small size, low loss and high efficiency, AC PMSM (Alternating Current Permanent Magnet Synchronous Motor) is widely used in the fields of Numerically-controlled machine tool, electric automobile, and robot, etc. Based on the research for its principle of work, its mathematical model and SVPWM (Space Vector Pulse Width Modulation), the author does a lot of theoretical and practical research for the tests as well as identification of PMSM's parameters. Besides, control strategies of PMSM based on the theory of ADRC (active disturbance rejection control) and Gray Theory are also studied deeply. Simulation and experiment results demonstrate the effectiveness of new ways to test and identify parameters, as well as ADRC.
A new method to test the several main parameters of PMSM is proposed in this paper. Since the magnet meter cannot be avoided in Voltage Integration method which is used to get Ld (direct-axis inductance) and Lq (quadrature-axis inductance) and considering the conditions of lab and worksite, this paper proposes an alternate method. Steps of this method are as follows. To begin with, data should be gathered by general devices of voltage collecting, such as oscilloscope. Then the discrete voltage signal can be integrated through Numerical-integration method to get PMSM's flux linkage which can be used to compute its inductance. This method is simple and easy to implement. Experiment results have proved the effectiveness of this new method.
Since Rs (wingding resistance), Ld and Lq will vary a lot with temperature, identification of these parameters are studied though mathematical analysis and simulation. The author has built a simulation model of PMSM in MATLAB whose parameters can vary online, and identified the parameters using EKF (Extended Kalman) filter & Elman neural network and RLS (Recursive Least Square) method. Despite that both of the two methods can guarantee high identification accuracy, they are unsuitable to identify online and have big compute loads. Based on the above disadvantages, WLS (Windowed Least Square) method is proposed. In this method, the length of the window can be adjusted according to practical conditions. Besides, it has a small computational burden. That is why it can guarantee the identification accuracy and excellent timeliness. And at the same time, the issue of data saturation of RLS can be avoided. Compared with simulation results of the other two methods, WLS's results show that this method is the most effective.
To improve the working stability and control performance of PMSM, ADRC is applied to reject disturbances from power supply and torque. Simulation results prove that the control performance of ADRC is better than PID control. Then, based on the ADRC theory, the author combines Optimal control, Neural networks and ADRC method together and proposes two algorithms named Self-optimizing ADRC method and ADRC based on neural networks, respectively. Compared with ADRC method, the Self-optimizing ADRC method has better control performance and least control energy. To eliminate the influence of varying disturbance like external load on the control performance of rotation speed, neural network is bedded in ADRC, which can approximately narrow the vary range of original system's parameters, and at the same time improve ADRC's ability of state estimation so that it is possible for ESO (Extended State Observer) to work with lighter load. What's more, by doing this, controller's computing and response speed will be improved. It is proved by study results that both of the methods can provide better control effect.
In order to identify and control parameters of PMSM online, the author builds a control model of PMSM and simulates change process of Rs, Ld and Lq with temperature varying from 20℃to 80℃. Then the parameters are identified online using WLS method while ADRC is applied in speed loop. Simulation results demonstrate that the performance of ADRC system using online estimation strategy is better than that of the system using only PID method.
In this paper, Gray Compensation PID algorithm and Gray Predict PID Control algorithm are applied in PMSM Servo System for the first time to control PMSM with less data, and results show that this control strategy has more satisfied robustness and adaptive capacity.
A set of AC PMSM Servo System has been developed to realize the SVPWM control based on three closed loop. The load test and no-load test prove that ADRC has the advantages of fast response, no over modulation, and strong ability of rejecting disturbance, compared with classical PID control.
In addition, since parameters of IGBT will change in work site, the author has developed a new kind of circuit which can modulate the turn-on and turn-off time of IGBT. Moreover, this driving circuit is also able to protect itself when power supply is under-voltage. And this kind of circuit is proved to be effective.
引文
[1]唐任远.现代永磁电机.第一版.北京.机械工业出版社.2006.5：1-12.
[2]刘锦波.电机与拖动.第一版.北京.清华大学出版社.2006.9：1.
[3]郭庆鼎等.现代永磁电动机交流伺服系统.第一版.北京.中国电力出版社.2006.8：60-63.
[4]李崇坚.交流同步电机调速系统.科学出版社.北京.第一版.2006.3：246.
[5]黄升华,吴芳.交流永磁伺服系统国内外发展概况.微特电机.2008年第五期：52-56.
[6]孙华建等.永磁同步电机伺服系统的现状及发展前景.中国西部科技.2009.6(上旬).第08卷第16期：33-34.
[7]陈伟华等.GB/T 22669-2008.2008-12-31发布.三相永磁同步电动机试验方法.北京.中国标准出版社.2009.5：4-19.
[8]徐照昌等.GB/T 1029-1993.三相同步电机试验方法.1993-06-28发布.北京.中国标准出版社.2003.3：2-4.
[9]王群京.稀土钕铁硼永磁同步电机的设计理论及计算机仿真.合肥.中国科学技术大学出版利.1997.12：103-112.
[10]唐任远等.一种基于小直流衰减法测试永磁电机电抗参数的装置.中国.实用新型.专利号200520091049.2006-12-20.
[11]唐任远等.一种基于电压积分法测试永磁电机电抗参数的装置.中国.实用新型.专利号200520091050.2006-10-25.
[12]王志贤编著.最优状态估计与系统辨识.第一版.西安.西北工业大学出版社.2004.6：153.
[13]王琳,马平.系统辨识方法综述.电力情报.2001第四期：63-66.
[14]孙频东等.基于EKF算法的交流永磁无刷同步电机参数辨识.南京师范大学学报(工程技术版)第8卷(第1期).2008年3月.
[15]Quntao An, Li Sun. On-line Parameter Identification for Vector Controlled PMSM Drives Using Adaptive Algorithm. Proceedings of IEEE Vehicle Power and Propulsion Conference (VPPC).Harbin,China.September 3-5,2008.
[16]丁坚勇,陈允平,基于ELMAN神经网络的同步电机动态参数在线辨识,电网技术,2002.Vol.26 No.4. pp:22-25.
[17]Wade, S., Dunnigan, M.W., Williams, B.W.. Modeling and simulation of induction machine vector control with rotor resistance identification. IEEE Transactions on Power Electronics. 1997. Vol.12 No.3. pp:495-506.
[18]Ashrafzadeh, F., Sachdeva, R., Chu, A.. A novel neural network controller and its efficient DSP implementation for vector controlled induction motor drives. Conference Record of the 2002 IEEE Industry Applications Conference.37th IAS Annual Meeting (Cat. No.02CH37344). Pittsburgh, PA, USA.2002. IEEE, Piscataway, NJ, USA.2002. pp:(vol.2)1455-1462.
[19]Ting-na Shi, Xiang-chao Wang, Chang-liang Xia, Qian Zhang. Adaptive speed control of PMSM based on wavelet neural network.2007 IEEE International Symposium on Industrial Electronics. Vigo, Spain.2007. IEEE, Piscataway, NJ, USA.2007. pp:2842-2847.
[20]Jun Zheng, Yunkuan Wang, Xiaofei Qin, Xin Zhang. An offline parameter identification method of induction motor.2008 7th World Congress on Intelligent Control and Automation. Chongqing, China.2008. IEEE, Piscataway, NJ, USA.2008. pp:8898-8901.
[21]Mondal SK, Pinto JOP, Bose BK. A neural-network-based space-vector PWM controller for a three-level voltage-fed inverter induction motor drive. IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS.2002. Vol.38 No.3. pp:660-669.
[22]Sadati, N., Barati, F.. Artificial neural network based implementation of space vector modulation for voltage fed inverter induction motor drive. IECON 2006.32nd Annual Conference on IEEE Industrial Electronics (IEEE Cat. No.06CH37763). Paris, France.2006. IEEE, Piscataway, NJ, USA.2006. pp:4410-4414.
[23]Wenxin Liu, Li Liu, Cartes, D.A.. Efforts on real-time implementation of PSO based PMSM parameter identification.2008 IEEE Power & Energy Society General Meeting. Pittsburgh, PA, USA.2008. IEEE, Piscataway, NJ, USA.2008. pp:7.
[24]Wenxin Liu, Li Liu, Cartes, D.A.. Particle swarm optimization as a general design tool in power engineering.2008 IEEE Power & Energy Society General Meeting. Pittsburgh, PA, USA.2008. IEEE, Piscataway, NJ, USA.2008. pp:8.
[25]Morimoto S, Shimmei A, Sanada M, Takeda Y. Position and speed sensorless control system of permanent magnet synchronous motor with parameter identification. ELECTRICAL ENGINEERING IN JAPAN. Vol.160 No.2. pp:68-76.
[26]Khan, M.A.S., Rahman, M.A.. Real-time implementation of IPM motor protection using artificial neural network. IECON 2007.33rd Annual Conference of the IEEE Industrial Electronics. Taipei, Taiwan.2007. IEEE, Piscataway, NJ, USA.2007. pp:1021-1026.
[27]Xianqing Cao, Liping Fan. Real-time PI controller based on pole assignment theory for permanent magnet synchronous motor. Proceedings of the IEEE International Conference on Automation and Logistics (ICAL). Qingdao, China.2008. IEEE, Piscataway, NJ, USA.2008. pp:211-215.
[28]Nahid-Mobarakeh B, Meibody-Tabar F, Sargos FM. State and disturbance observers in mechanical sensorless control of PMSM.2004 IEEE International Conference on Industrial Technology (ICIT), Vols.1-3. Hammamet, TUNISIA.2004. IEEE,345 E 47TH ST, NEW YORK, NY 10017 USA.2004. pp:181-186.
[29]Jong-Wook Kim, Taegyu Kim, Youngsu Park, Sang Woo Kim. On-load motor parameter identification using univariate dynamic encoding algorithm for searches. IEEE Transactions on Energy Conversion. Vol.23 No.3. pp:804-813.
[30]Thomsen, S., Rothenhagen, K., Fuchs, F.W.. Online parameter identification methods for doubly fed induction generators.2008 IEEE Power Electronics Specialists Conference. Rhodes, Greece.2008. IEEE, Piscataway, NJ, USA.2008. pp:2735-2741.
[31]Vaseghi B, Nahid-Mobarakeh B, Takorabet N, Meibody-Tabar F. Modeling of non-salient PM synchronous machines under stator winding inter-turn fault condition:Dynamic model-FEM model.2007 IEEE VEHICLE POWER AND PROPULSION CONFERENCE, Vols 1 and 2. Arlington, TX.2007. IEEE,345 E 47TH ST, NEW YORK, NY 10017 USA.2007. pp:635-640.
[32]Attaianese C, Damiano A, Gatto G, Marongiu I, Perfetto A. Induction motor drive parameters identification. IEEE TRANSACTIONS ON POWER ELECTRONICS. Vol.13 No.6. pp:1112-1122.
[33]Mondal, S., Pinto, J.O.P., Bose, B.K.. A neural network based space vector PWM controller for a three-level voltage-fed inverter induction motor drive. Conference Record of the 2001 IEEE Industry Applications Conference.36th IAS Annual Meeting (Cat. No.01CH37248). Chicago, IL, USA.2001. IEEE, Piscataway, NJ, USA.2001. pp:(vol.3) 1679:1686.
[34][美]夏天长,熊光愣.李芳芸译.系统辨识-最小二乘法.北京.清华大学出版社.1983：20-30.
[35]庞中华,崔红.系统辨识与自适应控制MATLAB仿真.第一版.北京.北京航空航天大学出版社.2009：27-42.
[36]Abjadi NR, Soltani J, Pahlavaninezhad M, Askari J. A nonlinear adaptive controller for speed sensorless PMSM taking the iron loss resistance into account. Proceedings of the Eighth International Conference on Electrical Machines and Systems, VOLS 1-3. (ICEMS 2005). Nanjing, CHINA.2005. INTERNATIONAL ACADEMIC PUBLISHERS LTD.2005. pp:188-193.
[37]Khov, M., Regnier, J., Faucher, J.. Detection of turn short-circuit faults in stator of PMSM by on-line parameter estimation.2008 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM). Ischia, Italy.2008. IEEE, Piscataway, NJ, USA. 2008. pp:161-166.
[38]Khov, M., Regnier, J., Faucher, J.. Monitoring of turn short-circuit faults in stator of PMSM in closed loop by on-line parameter estimation. Proceedings of the 2009 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives. (SDEMPED 2009). Cargese, France.2009. IEEE, Piscataway, NJ, USA.2009. pp:6
[39]Khov, M., Regnier, J., Faucher, J.. On-line parameter estimation of PMSM in open loop and closed loop.2009 IEEE International Conference on Industrial Technology. Gippsland, VIC, Australia.2009. IEEE, Piscataway, NJ, USA.2009. pp:6
[40]Luo Ruifu, Wang Limei, Guo Qingding. Direct neural network adaptive observer control for PMSM.1997 IEEE International Conference on Intelligent Processing Systems (Cat. No.97TH8335). Beijing, China.1997. IEEE, New York, NY, USA.1997. pp:(vol.1)414-418.
[41]Morimoto S, Sanada M, Takeda Y. Mechanical sensorless drives of IPMSM with online parameter identification. IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS.2006. Vol 42.No.5. pp:1241-1248.
[42]张志锋,郭庆鼎.基于状态观测器增益自调整的交流伺服系统.组合机床与自动化加工技术.2007年1期：66.
[43]Samuel J. Underwood. On-line parameter estimation and adaptive control of permanent magnet synchronous machines[Dissertation]. University of Akron.2006. pp:204p
[44]王松,刘明光等.基于卡尔曼滤波和神经网络的PMSM参数辨识.北京交通大学学报.2010.4.34(2)：124-127.
[45]Jiang Baojun. A novel algorithm based on EKF to estimate rotor position and speed for sensorless PMSM drivers.2009 International Conference on Information Engineering and Computer Science (ICIECS 2009). Wuhan China.2009. IEEE, Piscataway, NJ, USA.2009. pp:4pp.
[46]Qi Jilong, Tian Yantao, Guo Yimin, Zhucheng. A sensorless initial rotor position estimation scheme and an extended Kalman filter observer for the direct torque controlled permanent magnet synchronous motor drive.2008 11th International Conference on Electrical Machines and Systems (ICEMS 2008). Wuhan China.2008. IEEE, Piscataway, NJ, USA.2008. pp:3945-3950.
[47]Anbang Wang, Qunjing Wang, Cungang Hu, Zhe Qian, Lufeng Ju, Jun Liu. An EKF for PMSM sensorless control based on noise model identification using ant colony algorithm.200912th International Conference on Electrical Machines and Systems (ICEMS 2009). Tokyo, Japan. 2009. IEEE, Piscataway, NJ, USA.2009. pp:4pp.
[48]Yingpei Liu, Jianru Wan, Hong Shen, Guangye Li, Chenhu Yuan. PMSM speed sensorless direct torque control based on EKF.2009 4th IEEE Conference on Industrial Electronics and Applications. Xi'an, China,2009. IEEE, Piscataway, NJ, USA.2009. pp:3581-3584.
[49]Peroutka, Z., Smidl, V.,Vosmik, D.. Challenges and limits of extended Kalman Filter based sensorless control of permanent magnet synchronous machine drives.2009 13th European Conference on Power Electronics and Applications (EPE). Barcelona, Spain.2009. IEEE, Piscataway, NJ, USA.2009. pp:11pp.
[50]Byoung-Gun Park, Jin-Su Jang, Tae-Sung Kim, Dong-Seok Hyun. EKF-based fault diagnosis for open-phase faults of PMSM drives.2009 IEEE 6th International Power Electronics and Motion Control Conference. Wuhan, China.2009. IEEE, Piscataway, NJ, USA.2009. pp:418-422.
[51]Wang Song, Shi Shuang-shuang, Chen Chao, Yang Gang, Qu Zhi-jian. Identification of PMSM based on EKF and elman neural network.2009 IEEE International Conference on Automation and Logistics (1CAL). Shenyang, China.2009. IEEE, Piscataway, NJ, USA.2009. pp:1459-1463.
[52]Vyncke, T.J., Boel, R.K., Melkebeek, J.A.A.. On Extended Kalman Filters with Augmented State Vectors for the Stator Flux Estimation in SPMSMs.2010 Twenty-Fifth Annual IEEE Applied Power Electronics Conference and Exposition-APEC 2010. Palm Springs, CA, USA. 2010. IEEE, Piscataway, NJ, USA.2010. pp:1711-1718.
[53]Boileau, T., Nahid-Mobarakeh, B., Meibody-Tabar, F.. On-line identification of PMSM parameters:model-reference vs EKF.2008 IEEE Industry Applications Society Annual Meeting. Edmonton, Alta., Canada.2008. IEEE, Piscataway, NJ, USA.2008. pp:8pp.
[54]Jin-Su Jang, Byoung-Gun Park, Tae-Sung Kim, Dong Myung Lee, Dong-Seok Hyun. Sensorless control of four-switch three-phase PMSM drive using extended Kalman filter. IECON 2008-34th Annual Conference of IEEE Industrial Electronics Society. Orlando, FL, USA.2008. IEEE, Piscataway, NJ, USA.2008. pp:1368-1372.
[55]Qiu, A., Bin Wu, Kojori, H.. Sensorless control of permanent magnet synchronous motor using extended Kalman filter. Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No.04CH37513). Niagara Falls, Ont., Canada.2004. IEEE, Piscataway, NJ, USA. 2004. pp:(vol.3)1557-1562.
[56]Bolognani S, Oboe R, Zigliotto M. Sensorless full-digital PMSM drive with EKF estimation of speed and rotor position. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS.1999. Vol.46.No.1.pp:184-191.
[57]Yuan Xiao-ling, Wang Hong-hua. Intelligent sensorless control of permanent magnet synchronous motor drive.2009 Second International Conference on Intelligent Computation Technology and Automation (ICICTA). Changsha, Hunan, China.2009. IEEE, Piscataway, NJ, USA.2009. pp:454-457.
[58]Medagam, P.V., Yucelen, T., Pourboghrat, F.. Adaptive SDRE based nonlinear sensorless speed control for PMSM drives.2009 American Control Conference (ACC-09). St. Louis, MO, USA. 2009. IEEE, Piscataway, NJ, USA.2009. pp:3866-3871
[59]Yu Liu, Jie Liu, Li Dai, Chunming Yu. Sensorless speed estimation of PMSM using a hybrid method.2008 7th World Congress on Intelligent Control and Automation. Chongqing, China. 2008. IEEE, Piscataway, NJ, USA.2008. pp:3451-3454.
[60]Tze-Fun Chan, Borsje, P., Weimin Wang. Application of Unscented Kalman filter to sensorless permanent-magnet synchronous motor drive.2009 IEEE International Electric Machines and Drives Conference (IEMDC). Miami, FL, USA.2009. IEEE, Piscataway, NJ, USA.2009. pp:631-638.
[61]Huang, M.C., Moses, A.J., Anayi, F., Yao, X.G.. Linear Kalman filter (LKF) sensorless control for permanent magnet synchronous motor based on orthogonal output linear model.2006 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (IEEE Cat. No.06EX1320C). Taormina, Italy.2006. IEEE, Piscataway, NJ, USA.2006. pp:S14-31-S14-36.
[62]Jiangtao Wang, Haiqin Liu. Novel intelligent sensorless control of permanent magnet synchronous motor drive. Proceedings of the 2009 9th International Conference on Electronic Measurement & Instruments (ICEMI 2009). Beijing, China.2009. IEEE, Piscataway, NJ, USA. 2009. pp:2-953-2-958.
[63]Shan-Mao Gu, Feng-You He, Hui Zhang. Study on extend Kalman filter at low speed in sensorless PMSM drives.2009 International Conference on Electronic Computer Technology (ICECT 2009). Macau, China.2009. IEEE, Piscataway, NJ, USA.2009. pp:311-316.
[64]Changming Chen, Xi Xiao. Torque ripple minimization in permanent magnet synchronous motor magnet demagnetization. IEEE EUROCON 2009. St.-Petersburg, Russia.2009. IEEE, Piscataway, NJ, USA.2009. pp:843-848.
[65]Bolognani S, Tubiana L, Zigliotto M. Extended Kalman Filter tuning in sensorless PMSM drives. Proceedings of the power conversion confeerence-Osaka 2002, Vols I-III(PCC-OSAKA 2002). Osaka, Japan.2002. IEEE,345 E 47TH ST, NEW YORK, NY 10017 USA.2002. pp:276-281.
[66]Borsjie, P., Chan, T.F., Wong, Y.K., Ho, S.L.. A comparative study of Kalman filtering for sensorless control of a permanent-magnet synchronous motor drive. International Electric Machines and Drives Conference (IEEE Cat. No.05EX1023C). San Antonio, TX, USA.2005. IEEE, Piscataway, NJ, USA.2005. pp:815-822,
[67]李剑飞等.基于扩展卡尔曼滤波器的异步电机转速辨识.电工技术报.2002.17(5)：41-43.
[68]吴靖.电机传动系统参数辨识方法的研究[博士学位论文].杭州.浙江大学.2008.
[69]李永东,朱吴.永磁同步电机无速度传感器控制综述.电气传动.2009.39(9)：3-9.
[70]孙丹,贺益康.基于转子磁链观测的无速度传感器PMSM DTC.浙江大学学报(工学版).2006.40(7)：1276-1280.
[71]吴姗姗,李永东.基于信号注入的极低速PMSM无速度传感器控制.电气传动.2008.38(1)：19-22.
[72]侯利民等.PMSM无速度传感器最优转矩控制系统的研究.仪器仪表学报.2009.30(4)：706-710.
[73]文永明等.基于无位置传感器的永磁电机控制技术综述.微电机.2002.35(6)：32-P35.
[74]王久和.交流电动机的非线性控制.第一版.北京.电子工业出版社.2009.8：3-6.
[75]陈荣,严仰光.交流永磁伺服系统控制策略研究.电机与控制学报.2004.8(3)：205-208.
[76]洪奕光,程代展.非线性系统的分析与控制.第一版.北京.科学出版社.2006.9(：前言).
[77]Ortega, R., Loria, A., Nicklasson, P.J., Sira-Ramirez, H.J. Passivity-Based Control of Euler-Lagrange System:Mechanical Electrical and Electromechanical Applications [M]. Spring Verlag,1998
[78]Chiu-keng Lai, Kuo-kai Shyu. A novel motor drive design for incremental motion system via sliding-mode control method [J]. IEEE Trans on Industrial Electronics.2005.52(2):499-507.
[79]纪志成,薛花,沈艳霞.感应电动机无源性控制方法研究.电工技术学报.2005.20(3)：1-6.
[80]Hou Li-Min, Zhang Hua-Guang, etc. Adaptive fuzzy sliding mode soft switch of speed sensorless for PMSM based on robust passivity-based control. Control and Decision.2010.5v 25(5):686-690.
[81]韩京清.自抗扰控制技术-估计补偿不确定因素的控制技术.第一版.北京.国防工业出版社.2009.2.
[82]冯光,黄立培,朱东起.采用自抗扰控制器的高性能异步电机调速系统.中国电机工程学报.2001,21(10)：55-58.
[83]苏位峰,孙旭东,李发海.基于自抗扰控制器的异步电机矢量控制.清华大学学报(自然科学版).2004.44(10)：1329-1332.
[84]孙凯,许镇琳,邹积勇.基于自抗扰控制器的永磁同步电机无位置传感器矢量控制系统.中国电机学报.2007.27(3)：18-22.
[85]林飞,张春朋,宋文超.基于扩张状态观测器的感应电机转子磁链观测.中国电机工程学报.2003.23(4)：145-147.
[86]姚锡凡等.人工智能技术及应用.第一版.北京.中国电力出版社.2008.1：1-8.
[87]邓聚龙.灰色控制系统.第二版.武汉.华中理工大学出版社.1997.10.
[88]熊和金,徐华中.灰色控制.第一版.北京.国防工业出版社.2005.
[89]蔡明山.直线无刷直流调速系统的灰色PID控制.湘潭师范学院学报(自然科学版).27(2)：28-29.
[90]梁中华,韩殷,法乃光.永磁同步电机伺服控制系统的灰色PID控制.沈阳工业大学学报.2008.30(6)：619-622,638.
[91]Dong-feng Wang,Pu Han,Wei Han.Typical Grey Prediction Control Methods and Simulation Study.Proceeding of the second International Conference on Machine Learning and Cybernetics.2003:513-518.
[92]Dong-na Shi, Guo Peng, Teng-fei Li. Gray Predictive Adaptive Smith-PID Control and Its Application. Proceeding of the seventh International Conference on Machine Learning and Cybernetics.2008:1980-1984.
[93]王成元,夏加宽,孙宜标.电机现代控制技术.第一版.北京.机械工业出版社.2006.8：80-85.
[94]王松等.一种基于电压积分法测试永磁电机电抗参数的方法.中国.G01R31/34；G01R27/26.201010137824.2010.09.
[95]王兆安,张明勋.电力电子设备设计和应用手册.第二版.北京.机械工业出版社.2002.7：475.
[96]王晓明.电动机的DSP控制-TI公司DSP应用.第二版.北京.北京航空航天大学出版社.2009.9：166-177
[97]尚吉吉.永磁同步电动机磁场定向控制的研究[博士学位论文].杭州.浙江大学.2007.
[98]李波,安群涛,孙兵成,空间矢量脉宽调制的仿真研究及其实现,电机与控制应用,Vol.33, No.6,2006, pp:40-44.
[96]陈国呈.PWM逆变技术及应用.第一版.北京.中国电力出版社.2007：150-155.
[100]Xing Shaobang Zhao Keyou.Research on A Novel SVPWM Algorithm. Second IEEE Conference on Industrial Electronics and Applications. Harbin China.2007. IEEE, Piscataway, NJ, USA.2007. pp:1869-1872.
[101]邓自立.卡尔曼滤波与维纳滤波-现代时间序列分析方法.第一版.哈尔滨.哈尔滨工业大学出版社.2003.pp：56-57.
[102]付梦印,邓志红,张继伟Kalman滤波理论及其在导航系统中的应用.第一版.北京.科学出版社.2003.pp：22.
[103]周熙炜,刘卫国,郎宝华.异步电动机转子参数的在线辨识方法.微特电机.2006.Vol.12.pp18-20.
[104]沈艳霞,江俊,纪志成.基于递归神经网络的永磁同步电机控制器的设计.南京理工大学学报.2005.Vol.29.pp:73-76.
[105]张良均,曹晶,蒋世忠.神经网络实用教程.第一版.北京.机械工业出版社.2007.
[106]Wangsong. Parameter Identification of PMSM Based on Windowed Least Square Algorithm. The International Conference on Electrical Engineering and Automatic Control (ICEEAC2010).Zibo.Chengdu.Institute of Electrical and Electronics Engineering, Inc. 2010.11.26:129-132.
[107]Wang song.Active Disturbance Rejection Control with Optimal Index of Permanent Magnet Synchronous Motor.The International Conference on Electrical Engineering and Automatic Control (ICEEAC2010).Zibo.Chengdu.Institute of Electrical and Electronics Engineering, nc.2010.11.26:133-137.
[108]Yin Ying,Chen Sen-fa, Research on the Prediction Model to the Highway Transportation Demand Based on Moving Average and Grey Theory[J],Proceeding of 2007 IEEE International Conference on Grey Systems and Intelligent Services,2007,pp:735-738
[110]Wang song, Research on grey compensation-prediction PID controller for PMSM servo system. Chinese control and decision conference (CCDC).Xuzhou.2010. Xuzhou. IEEE Computer Society.2010.5:1425-1429.
[111]王松等.一种新型IGBT驱动保护电路.中国.201010216963.7.2010.11.
[2]刘锦波.电机与拖动.第一版.北京.清华大学出版社.2006.9：1.
[3]郭庆鼎等.现代永磁电动机交流伺服系统.第一版.北京.中国电力出版社.2006.8：60-63.
[4]李崇坚.交流同步电机调速系统.科学出版社.北京.第一版.2006.3：246.
[5]黄升华,吴芳.交流永磁伺服系统国内外发展概况.微特电机.2008年第五期：52-56.
[6]孙华建等.永磁同步电机伺服系统的现状及发展前景.中国西部科技.2009.6(上旬).第08卷第16期：33-34.
[7]陈伟华等.GB/T 22669-2008.2008-12-31发布.三相永磁同步电动机试验方法.北京.中国标准出版社.2009.5：4-19.
[8]徐照昌等.GB/T 1029-1993.三相同步电机试验方法.1993-06-28发布.北京.中国标准出版社.2003.3：2-4.
[9]王群京.稀土钕铁硼永磁同步电机的设计理论及计算机仿真.合肥.中国科学技术大学出版利.1997.12：103-112.
[10]唐任远等.一种基于小直流衰减法测试永磁电机电抗参数的装置.中国.实用新型.专利号200520091049.2006-12-20.
[11]唐任远等.一种基于电压积分法测试永磁电机电抗参数的装置.中国.实用新型.专利号200520091050.2006-10-25.
[12]王志贤编著.最优状态估计与系统辨识.第一版.西安.西北工业大学出版社.2004.6：153.
[13]王琳,马平.系统辨识方法综述.电力情报.2001第四期：63-66.
[14]孙频东等.基于EKF算法的交流永磁无刷同步电机参数辨识.南京师范大学学报(工程技术版)第8卷(第1期).2008年3月.
[15]Quntao An, Li Sun. On-line Parameter Identification for Vector Controlled PMSM Drives Using Adaptive Algorithm. Proceedings of IEEE Vehicle Power and Propulsion Conference (VPPC).Harbin,China.September 3-5,2008.
[16]丁坚勇,陈允平,基于ELMAN神经网络的同步电机动态参数在线辨识,电网技术,2002.Vol.26 No.4. pp:22-25.
[17]Wade, S., Dunnigan, M.W., Williams, B.W.. Modeling and simulation of induction machine vector control with rotor resistance identification. IEEE Transactions on Power Electronics. 1997. Vol.12 No.3. pp:495-506.
[18]Ashrafzadeh, F., Sachdeva, R., Chu, A.. A novel neural network controller and its efficient DSP implementation for vector controlled induction motor drives. Conference Record of the 2002 IEEE Industry Applications Conference.37th IAS Annual Meeting (Cat. No.02CH37344). Pittsburgh, PA, USA.2002. IEEE, Piscataway, NJ, USA.2002. pp:(vol.2)1455-1462.
[19]Ting-na Shi, Xiang-chao Wang, Chang-liang Xia, Qian Zhang. Adaptive speed control of PMSM based on wavelet neural network.2007 IEEE International Symposium on Industrial Electronics. Vigo, Spain.2007. IEEE, Piscataway, NJ, USA.2007. pp:2842-2847.
[20]Jun Zheng, Yunkuan Wang, Xiaofei Qin, Xin Zhang. An offline parameter identification method of induction motor.2008 7th World Congress on Intelligent Control and Automation. Chongqing, China.2008. IEEE, Piscataway, NJ, USA.2008. pp:8898-8901.
[21]Mondal SK, Pinto JOP, Bose BK. A neural-network-based space-vector PWM controller for a three-level voltage-fed inverter induction motor drive. IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS.2002. Vol.38 No.3. pp:660-669.
[22]Sadati, N., Barati, F.. Artificial neural network based implementation of space vector modulation for voltage fed inverter induction motor drive. IECON 2006.32nd Annual Conference on IEEE Industrial Electronics (IEEE Cat. No.06CH37763). Paris, France.2006. IEEE, Piscataway, NJ, USA.2006. pp:4410-4414.
[23]Wenxin Liu, Li Liu, Cartes, D.A.. Efforts on real-time implementation of PSO based PMSM parameter identification.2008 IEEE Power & Energy Society General Meeting. Pittsburgh, PA, USA.2008. IEEE, Piscataway, NJ, USA.2008. pp:7.
[24]Wenxin Liu, Li Liu, Cartes, D.A.. Particle swarm optimization as a general design tool in power engineering.2008 IEEE Power & Energy Society General Meeting. Pittsburgh, PA, USA.2008. IEEE, Piscataway, NJ, USA.2008. pp:8.
[25]Morimoto S, Shimmei A, Sanada M, Takeda Y. Position and speed sensorless control system of permanent magnet synchronous motor with parameter identification. ELECTRICAL ENGINEERING IN JAPAN. Vol.160 No.2. pp:68-76.
[26]Khan, M.A.S., Rahman, M.A.. Real-time implementation of IPM motor protection using artificial neural network. IECON 2007.33rd Annual Conference of the IEEE Industrial Electronics. Taipei, Taiwan.2007. IEEE, Piscataway, NJ, USA.2007. pp:1021-1026.
[27]Xianqing Cao, Liping Fan. Real-time PI controller based on pole assignment theory for permanent magnet synchronous motor. Proceedings of the IEEE International Conference on Automation and Logistics (ICAL). Qingdao, China.2008. IEEE, Piscataway, NJ, USA.2008. pp:211-215.
[28]Nahid-Mobarakeh B, Meibody-Tabar F, Sargos FM. State and disturbance observers in mechanical sensorless control of PMSM.2004 IEEE International Conference on Industrial Technology (ICIT), Vols.1-3. Hammamet, TUNISIA.2004. IEEE,345 E 47TH ST, NEW YORK, NY 10017 USA.2004. pp:181-186.
[29]Jong-Wook Kim, Taegyu Kim, Youngsu Park, Sang Woo Kim. On-load motor parameter identification using univariate dynamic encoding algorithm for searches. IEEE Transactions on Energy Conversion. Vol.23 No.3. pp:804-813.
[30]Thomsen, S., Rothenhagen, K., Fuchs, F.W.. Online parameter identification methods for doubly fed induction generators.2008 IEEE Power Electronics Specialists Conference. Rhodes, Greece.2008. IEEE, Piscataway, NJ, USA.2008. pp:2735-2741.
[31]Vaseghi B, Nahid-Mobarakeh B, Takorabet N, Meibody-Tabar F. Modeling of non-salient PM synchronous machines under stator winding inter-turn fault condition:Dynamic model-FEM model.2007 IEEE VEHICLE POWER AND PROPULSION CONFERENCE, Vols 1 and 2. Arlington, TX.2007. IEEE,345 E 47TH ST, NEW YORK, NY 10017 USA.2007. pp:635-640.
[32]Attaianese C, Damiano A, Gatto G, Marongiu I, Perfetto A. Induction motor drive parameters identification. IEEE TRANSACTIONS ON POWER ELECTRONICS. Vol.13 No.6. pp:1112-1122.
[33]Mondal, S., Pinto, J.O.P., Bose, B.K.. A neural network based space vector PWM controller for a three-level voltage-fed inverter induction motor drive. Conference Record of the 2001 IEEE Industry Applications Conference.36th IAS Annual Meeting (Cat. No.01CH37248). Chicago, IL, USA.2001. IEEE, Piscataway, NJ, USA.2001. pp:(vol.3) 1679:1686.
[34][美]夏天长,熊光愣.李芳芸译.系统辨识-最小二乘法.北京.清华大学出版社.1983：20-30.
[35]庞中华,崔红.系统辨识与自适应控制MATLAB仿真.第一版.北京.北京航空航天大学出版社.2009：27-42.
[36]Abjadi NR, Soltani J, Pahlavaninezhad M, Askari J. A nonlinear adaptive controller for speed sensorless PMSM taking the iron loss resistance into account. Proceedings of the Eighth International Conference on Electrical Machines and Systems, VOLS 1-3. (ICEMS 2005). Nanjing, CHINA.2005. INTERNATIONAL ACADEMIC PUBLISHERS LTD.2005. pp:188-193.
[37]Khov, M., Regnier, J., Faucher, J.. Detection of turn short-circuit faults in stator of PMSM by on-line parameter estimation.2008 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM). Ischia, Italy.2008. IEEE, Piscataway, NJ, USA. 2008. pp:161-166.
[38]Khov, M., Regnier, J., Faucher, J.. Monitoring of turn short-circuit faults in stator of PMSM in closed loop by on-line parameter estimation. Proceedings of the 2009 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives. (SDEMPED 2009). Cargese, France.2009. IEEE, Piscataway, NJ, USA.2009. pp:6
[39]Khov, M., Regnier, J., Faucher, J.. On-line parameter estimation of PMSM in open loop and closed loop.2009 IEEE International Conference on Industrial Technology. Gippsland, VIC, Australia.2009. IEEE, Piscataway, NJ, USA.2009. pp:6
[40]Luo Ruifu, Wang Limei, Guo Qingding. Direct neural network adaptive observer control for PMSM.1997 IEEE International Conference on Intelligent Processing Systems (Cat. No.97TH8335). Beijing, China.1997. IEEE, New York, NY, USA.1997. pp:(vol.1)414-418.
[41]Morimoto S, Sanada M, Takeda Y. Mechanical sensorless drives of IPMSM with online parameter identification. IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS.2006. Vol 42.No.5. pp:1241-1248.
[42]张志锋,郭庆鼎.基于状态观测器增益自调整的交流伺服系统.组合机床与自动化加工技术.2007年1期：66.
[43]Samuel J. Underwood. On-line parameter estimation and adaptive control of permanent magnet synchronous machines[Dissertation]. University of Akron.2006. pp:204p
[44]王松,刘明光等.基于卡尔曼滤波和神经网络的PMSM参数辨识.北京交通大学学报.2010.4.34(2)：124-127.
[45]Jiang Baojun. A novel algorithm based on EKF to estimate rotor position and speed for sensorless PMSM drivers.2009 International Conference on Information Engineering and Computer Science (ICIECS 2009). Wuhan China.2009. IEEE, Piscataway, NJ, USA.2009. pp:4pp.
[46]Qi Jilong, Tian Yantao, Guo Yimin, Zhucheng. A sensorless initial rotor position estimation scheme and an extended Kalman filter observer for the direct torque controlled permanent magnet synchronous motor drive.2008 11th International Conference on Electrical Machines and Systems (ICEMS 2008). Wuhan China.2008. IEEE, Piscataway, NJ, USA.2008. pp:3945-3950.
[47]Anbang Wang, Qunjing Wang, Cungang Hu, Zhe Qian, Lufeng Ju, Jun Liu. An EKF for PMSM sensorless control based on noise model identification using ant colony algorithm.200912th International Conference on Electrical Machines and Systems (ICEMS 2009). Tokyo, Japan. 2009. IEEE, Piscataway, NJ, USA.2009. pp:4pp.
[48]Yingpei Liu, Jianru Wan, Hong Shen, Guangye Li, Chenhu Yuan. PMSM speed sensorless direct torque control based on EKF.2009 4th IEEE Conference on Industrial Electronics and Applications. Xi'an, China,2009. IEEE, Piscataway, NJ, USA.2009. pp:3581-3584.
[49]Peroutka, Z., Smidl, V.,Vosmik, D.. Challenges and limits of extended Kalman Filter based sensorless control of permanent magnet synchronous machine drives.2009 13th European Conference on Power Electronics and Applications (EPE). Barcelona, Spain.2009. IEEE, Piscataway, NJ, USA.2009. pp:11pp.
[50]Byoung-Gun Park, Jin-Su Jang, Tae-Sung Kim, Dong-Seok Hyun. EKF-based fault diagnosis for open-phase faults of PMSM drives.2009 IEEE 6th International Power Electronics and Motion Control Conference. Wuhan, China.2009. IEEE, Piscataway, NJ, USA.2009. pp:418-422.
[51]Wang Song, Shi Shuang-shuang, Chen Chao, Yang Gang, Qu Zhi-jian. Identification of PMSM based on EKF and elman neural network.2009 IEEE International Conference on Automation and Logistics (1CAL). Shenyang, China.2009. IEEE, Piscataway, NJ, USA.2009. pp:1459-1463.
[52]Vyncke, T.J., Boel, R.K., Melkebeek, J.A.A.. On Extended Kalman Filters with Augmented State Vectors for the Stator Flux Estimation in SPMSMs.2010 Twenty-Fifth Annual IEEE Applied Power Electronics Conference and Exposition-APEC 2010. Palm Springs, CA, USA. 2010. IEEE, Piscataway, NJ, USA.2010. pp:1711-1718.
[53]Boileau, T., Nahid-Mobarakeh, B., Meibody-Tabar, F.. On-line identification of PMSM parameters:model-reference vs EKF.2008 IEEE Industry Applications Society Annual Meeting. Edmonton, Alta., Canada.2008. IEEE, Piscataway, NJ, USA.2008. pp:8pp.
[54]Jin-Su Jang, Byoung-Gun Park, Tae-Sung Kim, Dong Myung Lee, Dong-Seok Hyun. Sensorless control of four-switch three-phase PMSM drive using extended Kalman filter. IECON 2008-34th Annual Conference of IEEE Industrial Electronics Society. Orlando, FL, USA.2008. IEEE, Piscataway, NJ, USA.2008. pp:1368-1372.
[55]Qiu, A., Bin Wu, Kojori, H.. Sensorless control of permanent magnet synchronous motor using extended Kalman filter. Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No.04CH37513). Niagara Falls, Ont., Canada.2004. IEEE, Piscataway, NJ, USA. 2004. pp:(vol.3)1557-1562.
[56]Bolognani S, Oboe R, Zigliotto M. Sensorless full-digital PMSM drive with EKF estimation of speed and rotor position. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS.1999. Vol.46.No.1.pp:184-191.
[57]Yuan Xiao-ling, Wang Hong-hua. Intelligent sensorless control of permanent magnet synchronous motor drive.2009 Second International Conference on Intelligent Computation Technology and Automation (ICICTA). Changsha, Hunan, China.2009. IEEE, Piscataway, NJ, USA.2009. pp:454-457.
[58]Medagam, P.V., Yucelen, T., Pourboghrat, F.. Adaptive SDRE based nonlinear sensorless speed control for PMSM drives.2009 American Control Conference (ACC-09). St. Louis, MO, USA. 2009. IEEE, Piscataway, NJ, USA.2009. pp:3866-3871
[59]Yu Liu, Jie Liu, Li Dai, Chunming Yu. Sensorless speed estimation of PMSM using a hybrid method.2008 7th World Congress on Intelligent Control and Automation. Chongqing, China. 2008. IEEE, Piscataway, NJ, USA.2008. pp:3451-3454.
[60]Tze-Fun Chan, Borsje, P., Weimin Wang. Application of Unscented Kalman filter to sensorless permanent-magnet synchronous motor drive.2009 IEEE International Electric Machines and Drives Conference (IEMDC). Miami, FL, USA.2009. IEEE, Piscataway, NJ, USA.2009. pp:631-638.
[61]Huang, M.C., Moses, A.J., Anayi, F., Yao, X.G.. Linear Kalman filter (LKF) sensorless control for permanent magnet synchronous motor based on orthogonal output linear model.2006 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (IEEE Cat. No.06EX1320C). Taormina, Italy.2006. IEEE, Piscataway, NJ, USA.2006. pp:S14-31-S14-36.
[62]Jiangtao Wang, Haiqin Liu. Novel intelligent sensorless control of permanent magnet synchronous motor drive. Proceedings of the 2009 9th International Conference on Electronic Measurement & Instruments (ICEMI 2009). Beijing, China.2009. IEEE, Piscataway, NJ, USA. 2009. pp:2-953-2-958.
[63]Shan-Mao Gu, Feng-You He, Hui Zhang. Study on extend Kalman filter at low speed in sensorless PMSM drives.2009 International Conference on Electronic Computer Technology (ICECT 2009). Macau, China.2009. IEEE, Piscataway, NJ, USA.2009. pp:311-316.
[64]Changming Chen, Xi Xiao. Torque ripple minimization in permanent magnet synchronous motor magnet demagnetization. IEEE EUROCON 2009. St.-Petersburg, Russia.2009. IEEE, Piscataway, NJ, USA.2009. pp:843-848.
[65]Bolognani S, Tubiana L, Zigliotto M. Extended Kalman Filter tuning in sensorless PMSM drives. Proceedings of the power conversion confeerence-Osaka 2002, Vols I-III(PCC-OSAKA 2002). Osaka, Japan.2002. IEEE,345 E 47TH ST, NEW YORK, NY 10017 USA.2002. pp:276-281.
[66]Borsjie, P., Chan, T.F., Wong, Y.K., Ho, S.L.. A comparative study of Kalman filtering for sensorless control of a permanent-magnet synchronous motor drive. International Electric Machines and Drives Conference (IEEE Cat. No.05EX1023C). San Antonio, TX, USA.2005. IEEE, Piscataway, NJ, USA.2005. pp:815-822,
[67]李剑飞等.基于扩展卡尔曼滤波器的异步电机转速辨识.电工技术报.2002.17(5)：41-43.
[68]吴靖.电机传动系统参数辨识方法的研究[博士学位论文].杭州.浙江大学.2008.
[69]李永东,朱吴.永磁同步电机无速度传感器控制综述.电气传动.2009.39(9)：3-9.
[70]孙丹,贺益康.基于转子磁链观测的无速度传感器PMSM DTC.浙江大学学报(工学版).2006.40(7)：1276-1280.
[71]吴姗姗,李永东.基于信号注入的极低速PMSM无速度传感器控制.电气传动.2008.38(1)：19-22.
[72]侯利民等.PMSM无速度传感器最优转矩控制系统的研究.仪器仪表学报.2009.30(4)：706-710.
[73]文永明等.基于无位置传感器的永磁电机控制技术综述.微电机.2002.35(6)：32-P35.
[74]王久和.交流电动机的非线性控制.第一版.北京.电子工业出版社.2009.8：3-6.
[75]陈荣,严仰光.交流永磁伺服系统控制策略研究.电机与控制学报.2004.8(3)：205-208.
[76]洪奕光,程代展.非线性系统的分析与控制.第一版.北京.科学出版社.2006.9(：前言).
[77]Ortega, R., Loria, A., Nicklasson, P.J., Sira-Ramirez, H.J. Passivity-Based Control of Euler-Lagrange System:Mechanical Electrical and Electromechanical Applications [M]. Spring Verlag,1998
[78]Chiu-keng Lai, Kuo-kai Shyu. A novel motor drive design for incremental motion system via sliding-mode control method [J]. IEEE Trans on Industrial Electronics.2005.52(2):499-507.
[79]纪志成,薛花,沈艳霞.感应电动机无源性控制方法研究.电工技术学报.2005.20(3)：1-6.
[80]Hou Li-Min, Zhang Hua-Guang, etc. Adaptive fuzzy sliding mode soft switch of speed sensorless for PMSM based on robust passivity-based control. Control and Decision.2010.5v 25(5):686-690.
[81]韩京清.自抗扰控制技术-估计补偿不确定因素的控制技术.第一版.北京.国防工业出版社.2009.2.
[82]冯光,黄立培,朱东起.采用自抗扰控制器的高性能异步电机调速系统.中国电机工程学报.2001,21(10)：55-58.
[83]苏位峰,孙旭东,李发海.基于自抗扰控制器的异步电机矢量控制.清华大学学报(自然科学版).2004.44(10)：1329-1332.
[84]孙凯,许镇琳,邹积勇.基于自抗扰控制器的永磁同步电机无位置传感器矢量控制系统.中国电机学报.2007.27(3)：18-22.
[85]林飞,张春朋,宋文超.基于扩张状态观测器的感应电机转子磁链观测.中国电机工程学报.2003.23(4)：145-147.
[86]姚锡凡等.人工智能技术及应用.第一版.北京.中国电力出版社.2008.1：1-8.
[87]邓聚龙.灰色控制系统.第二版.武汉.华中理工大学出版社.1997.10.
[88]熊和金,徐华中.灰色控制.第一版.北京.国防工业出版社.2005.
[89]蔡明山.直线无刷直流调速系统的灰色PID控制.湘潭师范学院学报(自然科学版).27(2)：28-29.
[90]梁中华,韩殷,法乃光.永磁同步电机伺服控制系统的灰色PID控制.沈阳工业大学学报.2008.30(6)：619-622,638.
[91]Dong-feng Wang,Pu Han,Wei Han.Typical Grey Prediction Control Methods and Simulation Study.Proceeding of the second International Conference on Machine Learning and Cybernetics.2003:513-518.
[92]Dong-na Shi, Guo Peng, Teng-fei Li. Gray Predictive Adaptive Smith-PID Control and Its Application. Proceeding of the seventh International Conference on Machine Learning and Cybernetics.2008:1980-1984.
[93]王成元,夏加宽,孙宜标.电机现代控制技术.第一版.北京.机械工业出版社.2006.8：80-85.
[94]王松等.一种基于电压积分法测试永磁电机电抗参数的方法.中国.G01R31/34；G01R27/26.201010137824.2010.09.
[95]王兆安,张明勋.电力电子设备设计和应用手册.第二版.北京.机械工业出版社.2002.7：475.
[96]王晓明.电动机的DSP控制-TI公司DSP应用.第二版.北京.北京航空航天大学出版社.2009.9：166-177
[97]尚吉吉.永磁同步电动机磁场定向控制的研究[博士学位论文].杭州.浙江大学.2007.
[98]李波,安群涛,孙兵成,空间矢量脉宽调制的仿真研究及其实现,电机与控制应用,Vol.33, No.6,2006, pp:40-44.
[96]陈国呈.PWM逆变技术及应用.第一版.北京.中国电力出版社.2007：150-155.
[100]Xing Shaobang Zhao Keyou.Research on A Novel SVPWM Algorithm. Second IEEE Conference on Industrial Electronics and Applications. Harbin China.2007. IEEE, Piscataway, NJ, USA.2007. pp:1869-1872.
[101]邓自立.卡尔曼滤波与维纳滤波-现代时间序列分析方法.第一版.哈尔滨.哈尔滨工业大学出版社.2003.pp：56-57.
[102]付梦印,邓志红,张继伟Kalman滤波理论及其在导航系统中的应用.第一版.北京.科学出版社.2003.pp：22.
[103]周熙炜,刘卫国,郎宝华.异步电动机转子参数的在线辨识方法.微特电机.2006.Vol.12.pp18-20.
[104]沈艳霞,江俊,纪志成.基于递归神经网络的永磁同步电机控制器的设计.南京理工大学学报.2005.Vol.29.pp:73-76.
[105]张良均,曹晶,蒋世忠.神经网络实用教程.第一版.北京.机械工业出版社.2007.
[106]Wangsong. Parameter Identification of PMSM Based on Windowed Least Square Algorithm. The International Conference on Electrical Engineering and Automatic Control (ICEEAC2010).Zibo.Chengdu.Institute of Electrical and Electronics Engineering, Inc. 2010.11.26:129-132.
[107]Wang song.Active Disturbance Rejection Control with Optimal Index of Permanent Magnet Synchronous Motor.The International Conference on Electrical Engineering and Automatic Control (ICEEAC2010).Zibo.Chengdu.Institute of Electrical and Electronics Engineering, nc.2010.11.26:133-137.
[108]Yin Ying,Chen Sen-fa, Research on the Prediction Model to the Highway Transportation Demand Based on Moving Average and Grey Theory[J],Proceeding of 2007 IEEE International Conference on Grey Systems and Intelligent Services,2007,pp:735-738
[110]Wang song, Research on grey compensation-prediction PID controller for PMSM servo system. Chinese control and decision conference (CCDC).Xuzhou.2010. Xuzhou. IEEE Computer Society.2010.5:1425-1429.
[111]王松等.一种新型IGBT驱动保护电路.中国.201010216963.7.2010.11.