Design a PI Controller Based on PSO and GWO for a Brushless DC Motor

Abstract

Recently all the moving mechanical parts that are subjected to wear and cause errors in the future are replaced with the equivalent of electrical. A Brushless Direct Current (BLDC) motor is preferable compared to a brushed DC motor because it substitutes the unit of mechanical commutations with an electronic unit, enhancing dynamic properties, noise level, and efficiency. Since it is fairly inexpensive, simple in structure, and performs well, maximum BLDC motor drives use a Proportional-Integral PI controller for controlling the machine's speed. The major issue with the PI controller, on the other hand, is altering its parameters throughout the deployment. As a result, this work shows how to tune the PI controller settings of a BLDC motor drive using Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). The results of a comparison of PSO and GWO for BLDC motors were obtained. Simulation tests for the BLDC engine in MATLAB/Simulink environment show that both PSO and GWO of BLDC motor give good results, but the best is GWO in tested in terms of transient response under different mechanical loads and speeds.