With the wide application of intelligent control systems in industrial automation, brushless DC motors (BLDC) are gradually becoming an ideal choice in many key application fields due to their advantages such as high efficiency, high power density, low operating cost, and relatively simple control. This study proposes the design and implementation of a BLDC motor control system based on an Extended Kalman Observer (EKO) to address the challenges in the motor's dynamic performance and control accuracy. By using STM32F407IG6 as the control core, a hardware platform is built to realize high-speed data processing and real-time control. The motor's rotor position θ and rotational speed ωe are selected as the system's state variables. Using the Extended Kalman Filter (EKF) theory, the optimal estimation of the system's state variables at the next moment is obtained and output. Finally, the output values replace the signal values of the encoder, thus completing the sensorless control system for the BLDC motor. Experiments show that the error between the position estimation value of the Extended Kalman Observer (EKO) and the actual value is less than 1%, and the target speed can achieve fast tracking when rising from 100 rad/s to 1200 rad/s, which demonstrates the excellent performance of the EKO in the closed-loop control of motor speed.
Xiao et al. (Mon,) studied this question.