Sensorless control of marine permanent magnet synchronous propulsion motor based on adaptive extended Kalman filter

Gross error interference or noise statistical deviation is the main factor that leads to the accuracy deterioration of extended Kalman filter (EKF) in estimating the speed and rotor position of ship propulsion permanent magnet synchronous motor (PMSM). In this paper, an adaptive extended Kalman filter (AEKF) algorithm based on innovation sequence is proposed. In the proposed method, gross error interference is first added to EKF to analyze its influence on the observation accuracy. Then, the weighting coefficient is set in the calculation of innovation covariance. By adjusting the weight of the innovation covariance matrix at the adjacent time, the innovation covariance difference is calculated and then used in the calculation of Kalman gain. The observation performance comparison between AEKF and EKF strategies is conducted with subject to gross error interference and noise statistics deviate. Simulation and experimental results demonstrate that the proposed AEKF has stronger robustness and higher prediction accuracy of speed and rotor position.