基于自适应插值扩展卡尔曼滤波的永磁同步电机状态估计

doi:10.16180/j.cnki.issn1007-7820.2020.05.011

电子科技 ›› 2020, Vol. 33 ›› Issue (5): 66-71.doi: 10.16180/j.cnki.issn1007-7820.2020.05.011

基于自适应插值扩展卡尔曼滤波的永磁同步电机状态估计

朱军,李紫豪,刘炳辰,孟祥斌,张哲

河南理工大学电气工程与自动化学院,河南焦作 454000

收稿日期:2019-03-14 出版日期:2020-05-15 发布日期:2020-06-02
作者简介:朱军(1984-),男,博士,副教授。研究方向:新能源风力发电机及无传感控制理论。|李紫豪(1992-),男,硕士研究生。研究方向:旋转电机及其控制。|刘炳辰(1993-),男,硕士研究生。研究方向:直线电机及其控制。|孟祥宾(1991-),男,硕士研究生。研究方向:电机电器及其控制。|张哲(1995-),男,硕士研究生。研究方向:电机电器及其控制。
基金资助:
国家自然科学基金(U1504506);河南省高校基本科研业务费专项资金资助项目(NSFRF140115)

State Estimation for Permanent Magnet Synchronous Motor Based on Adaptive Interpolation Extended Kalman Filter

ZHU Jun,LI Zihao,LIU Bingchen,MENG Xiangbin,ZHANG Zhe

School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo 454000,China

Received:2019-03-14 Online:2020-05-15 Published:2020-06-02
Supported by:
National Natural Science Foundation of China(U1504506);Special Funds Subsidy Project for Basic Scientific Research Business Fees in Colleges and Universities in Henan Province(NSFRF140115)

摘要/Abstract

摘要：

PMSM无传感控制的转速与转子位置估计对系统至关重要,扩展EKF算法作为无传感控制技术被广泛应用在工业领域。但是EKF算法在系统线性化过程中产生截断误差,对于高度非线性模型无法得到精确估计值。为减小EKF算法因非线性问题而造成的误差,文中提出了一种基于AIEKF状态估计法。该方法以量化状态方程的非线性程度为依据,通过添加伪状态值减小EKF算法线性化中产生的误差对估值精度的影响,从而降低系统在线性化过程引起的误差。仿真计算结果表明,AIEKF较EKF的截断误差平均降低了55.6%。

关键词: 永磁同步电机, 扩展卡尔曼滤波算法, 截断误差, 高度非线性, 自适应插值, 估计精度

Abstract:

Speed and rotor position estimation are critical for PMSM non-sensing control system. The EKF algorithm has been widely used in the industrial field as a sensorless control technology. However, the EKF algorithm produces truncation errors during system linearization, and accurate estimates cannot be obtained for highly nonlinear models. In order to reduce the error caused by nonlinear problem of EKF algorithm, based on AIEKF, a state estimation for PMSM was proposed in the study. Based on the degree of nonlinearity of the quantized state equation, this method reduced the influence of the error generated in the linearization of EKF algorithm on the estimation accuracy by adding pseudo state value, thereby reducing the errors caused by the linearization process of the system. Finally, simulation showed that the interception error of AIEKW was lower than EKF by 55.6%.

Key words: permanent magnet synchronous motor, extendedkalman filtering algorithm, truncationerrors, highly nonlinear, adaptive interpolation, estimation accuracy

中图分类号:

TN787

朱军,李紫豪,刘炳辰,孟祥斌,张哲. 基于自适应插值扩展卡尔曼滤波的永磁同步电机状态估计[J]. 电子科技, 2020, 33(5): 66-71.

ZHU Jun,LI Zihao,LIU Bingchen,MENG Xiangbin,ZHANG Zhe. State Estimation for Permanent Magnet Synchronous Motor Based on Adaptive Interpolation Extended Kalman Filter[J]. Electronic Science and Technology, 2020, 33(5): 66-71.

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参数	数值
额定功率/W	400
额定电流/A	2.5
额定转矩/N·m	1.27
额定转速/r·min^-1	3 000
定子电阻R_s/Ω	2.75
定子电感L_s/mH	12.54
磁链幅值ψ/Wb	0.175
极对数P_n	4
额定电压/V	220

基于自适应插值扩展卡尔曼滤波的永磁同步电机状态估计

State Estimation for Permanent Magnet Synchronous Motor Based on Adaptive Interpolation Extended Kalman Filter

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