Sensorless Control of SPMSM with Improved Extended Kalman Filter Based on Runge-Kutta Model

In order to achieve precise control of high-speed permanent magnet synchronous motors (PMSMs), the motor control system requires accurate rotor position information. The Extended Kalman Filter (EKF) method is widely used in sensorless control of PMSMs due to its excellent estimation accuracy, dynamic performance, and anti-interference capability. This paper investigates the impact of using the Euler approximation method on the EKF algorithm and proposes an improved EKF algorithm based on the Runge-Kutta method. The proposed algorithm effectively reduces the position estimation error in the EKF prior estimation process. Additionally, considering the motor system model during the acceleration process of the PMSM, an EKF algorithm that incorporates the speed differential is proposed, which enhances the performance of the algorithm during acceleration. Finally, the effectiveness of the proposed algorithm is validated in the MATLAB simulation platform.