Brushless DC motor control based on improved single neuron PID algorithm

When the conventional PID algorithm is used to control brushless DC motor, the parameter adjustment is difficult and the overshoot is large. This study proposed an improved single neuron PID control method to control brushless DC motor. The self-learning ability of neurons is used to adjust the parameters of conventional PID algorithm online. The parameters of single neuron PID algorithm are optimized by Flower Pollination Algorithm to find the optimal control parameters (etaP, etaI, etaD, K). The optimal parameters in this study are: etaP=640.776778, etaI=0.000239287602, etaD=38353.5126, K=24.8477395. The results verify that the improved control algorithm is superior to the conventional PID algorithm and single neuron algorithm in the motor control.