Algorithm Research & Explore
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1006-1009,1014

Method of EKF speed estimation for sensorless PMSM based on IGSO optimizing

Zhang Xiangsheng
Tian Jiawen
Pan Feng
Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi Jiangsu 214122, China

Abstract

In order to improve the speed control performance in sensorless permanent magnet synchronous motor(PMSM) control system, this paper proposed an extended Kalman filter(EKF) speed estimation scheme based on improved group search optimization(IGSO) algorithm. Firstly, it analyzed the PMSM field-oriented control(FOC) system model. Then, it used the motor's d-q axis voltage, current and rotor speed as state variables to construct the state equation, so as to use the EKF to estimate the speed and load. At the same time, in order to improve the estimated performance of EKF, it estimated the square error integral(ISE) between the value and the actual value as the fitness function, and used the IGSO algorithm to optimize the noise covariance matrix Q and R in EKF. The simulation results show that the proposed control system can accurately estimate the motor speed and control effectively.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.10.0989
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 4
Section: Algorithm Research & Explore
Pages: 1006-1009,1014
Serial Number: 1001-3695(2019)04-011-1006-04

Publish History

[2019-04-05] Printed Article

Cite This Article

张相胜, 田佳文, 潘丰. 无传感器PMSM中基于IGSO优化EKF的速度估计方法 [J]. 计算机应用研究, 2019, 36 (4): 1006-1009,1014. (Zhang Xiangsheng, Tian Jiawen, Pan Feng. Method of EKF speed estimation for sensorless PMSM based on IGSO optimizing [J]. Application Research of Computers, 2019, 36 (4): 1006-1009,1014. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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