BLDC speed control system based on neuron PID

In order to solve the problems of long adjustment time and weak anti-interference, a brushless DC motor speed control system based on neuron PID was proposed. Improved Hebb learning algorithm with property of self-learning and self-organizing was applied for the online adjust of PID control parameters. The step response of brushless DC motor speed control system was measured when the control system parameters change dynamically. The three parameters of the PID controller can be adjusted in real time and the rotational speed of the brushless DC motor can be effectively controlled. The results show that this control method has strong adaptability to coupling system and strong anti-interference ability.