Parameter Identification for PMSM Based on Multi-innovation Approximate Least Absolute Deviation Identification Algorithm

doi:10.16182/j.issn1004731x.joss.201803029

Abstract

Abstract: In view of the problem that the results of traditional identification algorithm are not accurate caused by the peak noise signal in the environment, a new algorithm based on the forgetting factor multi-innovation approximate least absolute deviation (MIALAD) identification algorithm is proposed. Combined with the system voltage equation of permanent magnet synchronous motor (PMSM), a discrete identification model is constructed. By using vector control method, the input and output data of the identification model are obtained to identify the rotor resistance and inductance. The simulation results show that this identification algorithm can obtain the accurate parameters of the PMSM model in the peak noise environment.

Key words: permanent magnet synchronous motor (PMSM), SVPWM vector control, forgetting factor, multi-innovation approximate least absolute deviation algorithm, parameter identification

CLC Number:

Wu Dinghui, Zhang Jianyu, Shen Yanxia, Ji Zhicheng. Parameter Identification for PMSM Based on Multi-innovation Approximate Least Absolute Deviation Identification Algorithm[J]. Journal of System Simulation, 2018, 30(3): 1001-1007.

References 19

[1]	殷战稳, 司纪凯. 永磁直线同步电机应用现状综述[J]. 微电机, 2012, 45(1): 76-80.Yin Zhanwen, Si Jikai. Application Status Reviews of PLLSM[J]. Micro Motors, 2012, 45(1): 76-80.
[2]	柏建勇, 滕青芳, 杜永栋, 等. 永磁同步电机控制策略比较研究[J]. 控制工程, 2015, 22(3): 490-494.BAI Jian-yong, TENG Qing-fang, DU Yong-dong, et al. Comparison of Control Strategies for Permanent Magnet Synchronous Motor[J]. Control Engineering of China, 2015, 22(3): 490-494.
[3]	孔小兵, 刘向杰. 永磁同步电机高效非线性模型预测控制[J]. 自动化学报, 2014, 40(9): 1958-1966.Kong Xiaobing,Liu Xiangjie. Efficient Nonlinear Model Predictive Control for Permanent Magnet Synchronous Motor[J]. Acta Automatica Sinica, 2014, 40(9): 1958-1966.
[4]	Rongmin C, Huixing Z, Zhongsheng H, et al.Low-speed Performance Research for Permanent Magnet Synchronous Linear Motor Based on Nonparametric Model-learning Adaptive Control[C]. Electrical Machines and Systems (ICEMS), 2011 International Conference on. IEEE, 2011: 1-5.
[5]	邱鑫, 黄文新, 杨建飞, 等. 一种基于转矩角的永磁同步电机直接转矩控制[J]. 电工技术学报, 2013, 28(3): 56-62.Qiu Xin, Huang Wenxin, Yang Jianfei, et al. A Direct Torque Control Strategy Based on Torque Angle for Permanent Synchronous Motors[J]. Transactions of China Electrotechnical Society, 2013, 28(3): 56-62.
[6]	曹杨. 永磁同步电机参数辨识研究[D]. 济南: 山东大学, 2014.Cao Yang. Research on Parameter Identification of Permanent Magnet Synchronous Motor[D]. Ji’nan: Shandong University, 2014.
[7]	汤斯. 永磁同步电机参数辨识的研究[D]. 武汉: 华中科技大学, 2009.Tan Si. The Research on Parameter Identification of Permanent Magnet Synchronous Motor[D]. Wuhan: Huazhong University of Science & Technology, 2009.
[8]	Wu R.Least absolute deviation estimation for general fractionally integrated autoregressive moving average time series models[J]. Statistics & Probability Letters (S0167-7152), 2014, 94: 98-112.
[9]	Xu B C, Zhang Y D.New identification method for Hammerstein models based on approximate least absolute deviation[J]. International Journal of Systems Science (S0020-7721), 2016, 47(9): 2201-2213.
[10]	Yousefi I, Ghanbari M.Parameter Estimation of Permanent Magnet Synchronous Motor Using Orthogonal Projection and Recursive Least Squares Combinatorial Algorithm[J]. Mathematical Problems in Engineering (S1024-123X), 2015, 2015(1): 1-7.
[11]	Xu Y, Lei Y, Sha D.Backstepping direct torque control of permanent magnet synchronous motor with RLS parameter identification[C]. Electrical Machines and Systems (ICEMS), 2014 17th International Conference on. IEEE, 2015: 573-578.
[12]	赵海森, 杜中兰, 刘晓芳, 等. 基于递推最小二乘法与模型参考自适应法的鼠笼式异步电机转子电阻在线辨识方法[J]. 中国电机工程学报, 2014, 34(30): 5386-5394.ZHAO Haisen, DU Zhonglan, LIU Xiaofang, et al. An On-line Identification Method for Rotor Resistance of Squirrel Cage Induction Motors Based on Recursive Least Square Method and Model Reference Adaptive System[J]. Proceedings of the CSEE, 2014, 34(30): 5386-5394.
[13]	王庆龙, 张兴, 张崇巍. 永磁同步电机矢量控制双滑模模型参考自适应系统转速辨识[J]. 中国电机工程学报, 2014, 34(6): 897-902.Wang Qinglong, Zhang Xing, Zhang Chongwei. Double Sliding-mode Model Reference Adaptive System Speed Identification for Vector Control of Permanent Magnet Synchronous Motors[J]. Proceedings of the SCEE, 2014, 34(6): 897-902.
[14]	刘英玉, 申东日, 陈义俊, 等. 基于前向神经网络的多新息随机梯度辨识算法[J]. 哈尔滨商业大学学报(自然科学版), 2006, 22(2): 83-86.LIU Yingyu , SHEN Dongri ,CHEN Yijun , et al. Multi- nnovation stochastic gradient identification algorithm based on feedforward neural networks[J]. Journal of Harbin University of Commerce (Natural Sciences Edition), 2006, 22(2): 83-86.
[15]	李洪宇, 王群京, 李国丽, 等. 基于扩展卡尔曼滤波器的电机参数辨识算法[J]. 电气工程学报, 2015, 10(5): 34-42.Li Hongyu, Wang Qunjing, Li Guoli, et al. Electromagnetic Parameter Identification Algorithm of AC Motor Based on Extended Kalman Filter[J]. Journal of Electrical Engineering, 2015, 10(5): 34-42.
[16]	Ding F, Chen H, Li M.Multi-innovation Least Squares Identification Methods Based on The Auxiliary Model for MISO Systems[J]. Applied Mathematics and Computation (S0096-3003), 2007, 187(2): 658-668.
[17]	徐宝昌, 张瀛丹. 基于近似最小一乘准则的多新息辨识算法[J]. 控制工程, 2015, 22(1): 60-65.XU Baochang, ZHANG Yingdan. Multi-innovation identification al-gorithm based on approximate least absolute deviation [J]. Control Engineering of China, 2015, 22(1): 60-65.
[18]	王成波, 朱熀秋, 吉裕晖. 基于SVPWM的无轴承永磁同步电机转子磁场定向控制系统研究[J]. 电工电能新技术, 2010, 29(2): 48-52.Wang Chengbo, Zhu Huangqiu, Ji Yuhui. Study on rotor field oriented control system of bearingless PMSM based on SVPWM [J].Advanced Technology of Electrical Engineering and Energy, 2010, 29(2):48-52
[19]	Hemalatha G, Chinnaiyan V K.Performance Analysis of Permanent Magnet Synchronous Motor Using SVPWM Technique[C]. Green Computing Communication and Electrical Engineering (CGCCEE), 2014 International Conference on. IEEE, 2014: 1-5.