Abstract: In order to solve the poor accuracy of the speed and rotor position of permanent magnet synchronous motor caused by sensor, a sensorless control system is proposed to calculate the speed and rotor position of PMSM with extended Kalman filtering algorithm. BP neural network algorithm is used to optimize the covariance matrix Q and R of EKF, which improves the accurate calculation values of rotational speed and rotor position. At the same time, the speed sliding mode controller combined with the current feed-forward decoupling unit are used to improve the stability of the whole control system. The simulation results show that the system can accurately calculate speed and rotor position and the deviation value of rotor position fluctuates around ±0.3 rad. Compared with the traditional PI control, the speed recovery time is shortened by 50%, and the overshoot is very small, the robustness is stronger. It has strong practical application value in motor control.

Key words: permanent magnet synchronous motor, extended Kalman filtering, BP(Back-ProPagation) neural network, speed sliding mode, feed-forward decoupling