Speed Estimation Strategy for Closed-Loop Control of PMSM Based on PSO Optimized KF Series Algorithms

Abstract

In this paper, solving the problem of the noise covariance matrix parameters tuning of the extended Kalman filter (EKF) and unscented Kalman filter algorithms (UKF) is difficult. A speed estimation strategy for a permanent magnet synchronous motor (PMSM) based on particle swarm optimization (PSO) optimized Kalman filter (KF) series algorithms is proposed. By using MATLAB/Simulink, in this paper, 20 effective simulation experiments on the noise covariance matrix parameter optimization process are conducted to obtain the optimal covariance matrix parameters of the extended Kalman filter and unscented Kalman filter. Moreover, EKF, PSO-EKF, UKF, and PSO-UKF are also compared to verify the effectiveness of the particle swarm optimization algorithm in optimizing the systems using the extended Kalman filter and unscented Kalman filter. For the error of speed estimation, taking 4000 rpm as a reference, the system using PSO-EKF has improved by 2.125% compared to that using EKF, and the system applying PSO-UKF has improved by 0.55% compared to the system applying UKF. For the error of electrical angle estimation, taking the system errors of original algorithms as references, the system adopting PSO-EKF has decreased by 60% compared to that adopting EKF, and the system using PSO-UKF has decreased by 47% compared to the system using UKF.