Research on Permanent Magnet Synchronous Motor Control System Based on Adaptive Kalman Filter

Abstract

A sensorless control system of a permanent magnet synchronous motor based on an extended Kalman filter (EKF) algorithm faces problems with inaccurate or mismatched process noise statistics. This problem affects the performance of the filter, resulting in an inaccurate estimation of motor speed. To address the above problem, this paper proposes a parameter-adaptive Kalman filter algorithm that does not depend on precise noise system covariance. This method can significantly reduce the negative impact of the noise statistical mismatch on motor speed estimation. In addition, the method uses adaptive covariance prediction and removes the original covariance checks in the EKF, thus reducing the calculation burden. The simulation results show that, compared with the traditional EKF algorithm, the algorithm proposed in this article can effectively reduce the steady-state jitter and improve the filtering adaptability and calculation accuracy.