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

Abstract

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

CLC Number:

TN787

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.

Figures/Tables 9

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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

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

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