Model prediction torque control of PMSM based on extended Kalman filter parameter identification

被引：0
作者：
Li H. [1 ]
Xu H. [1 ]
Xu Y. [1 ]
机构：
[1] School of Electrical and Information Engineering, Tianjin University, Tianjin
来源：
Dianji yu Kongzhi Xuebao/Electric Machines and Control | 2023年 / 27卷 / 09期
关键词：
deadbeat control; extended Kalman filter; model predictive torque control; permanent magnet synchronous motor; robust analysis; space vector pulse width modulation;
D O I：
10.15938/j.emc.2023.09.003
中图分类号：
学科分类号：
摘要：
In order to improve the steady-state performance of model predictive torque control, a deadbeat model predictive torque control based on space vector pulse width modulation (SVPWM) modulation strategy was proposed. The important influence of stator resistance and inductance on the control effect of the deadbeat algorithm was analyzed. To solve this problem, extended Kalman filter was used to identify the stator resistance and inductance parameters in the model simultaneously to improve the parameter robustness of the algorithm. Firstly, the reference voltage vector in the two-phase rotating coordinate system was obtained by using the deadbeat control of electromagnetic torque and stator flux amplitude, and then sent to SVPWM for modulation, so as to effectively suppress the torque ripple. Then, it was deduced that the output voltage of dq axis deviates seriously from the ideal value when the stator resistance and inductance have force majeure error. Finally, the inductance and resistance parameters in the model were identified online by using the extended Kalman filter, and the identification results were fed back to the model in real time. The steady-state performance, dynamic performance and parameter robustness of the proposed algorithm were compared with the published results by simulation and experiment, which proves the superiority of the proposed algorithm. © 2023 Editorial Department of Electric Machines and Control. All rights reserved.
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页码：19 / 30
页数：11
相关论文
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