Position Estimation Method of a Permanent Magnet Synchronous Motor Based on Moving Horizon Estimation EKF Algorithm

Abstract

The extended Kalman filter (EKF) is widely applied in the permanent magnet synchronous motor (PMSM) position estimation method. However, the estimation accuracy will be degraded, when measurement noise is not 0-mean random noise. To solve this problem, this article proposes an EKF algorithm with moving horizon estimation (MHE) to estimate the rotor position of PMSM more accurately. The proposed MHE EKF algorithm uses the concept of a moving time-domain window to estimate the motor operating status by integrating the window information of the N moments. By establishing a cost function and adding random noise to replace the measurement error, the prediction problem is transformed into an optimization problem. The simulation and experiment results show that this algorithm can effectively improve the accuracy of estimation position.