volume | PIER Journals

Abstract

In order to solve the issues of large computation and control performance affected by motor parameters in the conventional model predictive torque control (MPTC) of permanent magnet synchronous motors (PMSMs), a robust model predictive torque control strategy with online parameter identification based on an improved differential evolution extended Kalman filter (IDEEKF-RMPTC) is proposed To begin with, and a steady-state voltage vector at the next time is obtained through a low-pass filter and used as the reference voltage vector to select the alternative voltage vector. The parameter robustness of the PMSM system is enhanced, and the computational effort is reduced. In addition, an improved differential evacuation algorithm for the extended Kalman filter (EKF) is designed, and the system noise matrix Q and the measurement noise matrix R of the EKF are optimized. The estimation error is reduced; the stability of the system is enhanced; and the accuracy of the identification of the motor parameters is improved. Finally, the computational effort of the system is effectively reduced by the proposed IDEEKF-RMPTC strategy, and the parameter robustness of the PMSM drive system under parameter mismatch conditions is enhanced which are proved by the experimental results.

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Dr. Yang Zhang

Dr. Chenhui Liu

Dr. Sicheng Li

Dr. Kun Cao

Dr. Yiping Yang

Dr. Zhun Cheng