Tuning of Extended Kalman Filter using Self-adaptive Differential Evolution Algorithm for Sensorless Permanent Magnet Synchronous Motor Drive

In this paper, a novel method based on a combination of Extended Kalman Filter (EKF) with Self-adaptive Differential Evolution (SaDE) algorithm to estimate rotor position, speed and machine states for a Permanent Magnet Synchronous Motor (PMSM) is proposed. In the proposed method, as a first step SaDE algorithm is used to tune the noise covariance matrices of state noise and measurement noise in off-line. In the second step, the optimized values of above covariance matrices are injected into EKF in order to estimate the rotor speed on-line. The estimated speed is fed back to the PI controller and to minimize the speed error, parameters of PI controller are tuned again using SaDE algorithm. The simulation results show that the tuned covariance matrices Q and R improve convergence of estimation process, quality of estimated states and PI controller improves the settling time and stability of the system.