Title: Design of model predictive controllers for PMSM drive system based on the extended Kalman filter observer

Authors: Hongyu Shu; Cheng Guo; Yitong Song; Xianbao Chen; Shuang Luo

Addresses: State Key Lab of Mechanical Transmission, Chongqing University, Chongqing, 400044, China ' State Key Lab of Mechanical Transmission, Chongqing University, Chongqing, 400044, China ' State Key Lab of Mechanical Transmission, Chongqing University, Chongqing, 400044, China ' State Key Lab of Mechanical Transmission, Chongqing University, Chongqing, 400044, China ' State Key Lab of Mechanical Transmission, Chongqing University, Chongqing, 400044, China

Abstract: Considering some technical and economic reasons, it is not easy to directly measure the time-varying states which have a great impact on speed regulation performance in permanent magnet synchronous motor (PMSM) drive system. This paper proposes a method to estimate rotor speed, rotor position and load torque disturbance by utilising real-time estimations, based on the extended Kalman filter (EKF). In order to guarantee the accuracy and stability of PMSM drive system while running under load torque disturbance, this paper proposes a cascade closed-loop control strategy as a solution. The entire system is designed to track reference speed trajectory based on the estimated rotor speed, rotor position and load torque, using the model predictive control (MPC) algorithm based on linear PMSM model. Simulation results demonstrate the effectiveness of the states observer, and the cascade MPC structure shows the good performance in speed tracking of PMSM drive system.

Keywords: time-varying states; PMSM; permanent magnet synchronous motors; real-time estimations; EKF; extended Kalman filter; load torque disturbance; cascade; closed-loop control; MPC; model predictive control; states observer; speed tracking.

DOI: 10.1504/IJEHV.2019.102887

International Journal of Electric and Hybrid Vehicles, 2019 Vol.11 No.4, pp.378 - 394

Received: 02 Feb 2019
Accepted: 12 Jun 2019

Published online: 27 Sep 2019 *

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