State Estimation for Brushless DC Motor Based on Cubature Kalman Filter

Brushless DC motor (BLDCM) is widely used in industrial equipment due to high efficiency, easy control and simple structure. In order to ensure the precise control of the motor, real-time measurement of the motor's operating state is required. In this paper, a sensorless state estimation method for BLDCM is proposed. In view of the nonlinear characteristics of BLDCM, the traditional Kalman filter is no longer applicable, so the idea of Cubature Kalman filter (CKF) is utilized to derive the BLDCM state equation, and the covariance matrix and the state estimation value are updated in real time by inputting the BLDCM measurement data. Simulation results show that the proposed state estimation method can effectively estimate the motor speed and rotor position.