DETERMINATION OF PI COEFFICIENTS IN SPEED CONTROL OF BRUSHLESS DC MOTOR WITH GRAY WOLF OPTIMIZATION AND FPGA APPLICATION

Abstract

DC motors are widely utilized in various industries due to their efficiency, longevity, and adjustable speed settings. Effective control of these motors is crucial, given their broad application range. As applications vary, so do the controlled motor parameters, necessitating control systems that are suitable for industrial use. However, standard controllers often face challenges due to the non-linear and uncertain nature of the mathematical models involved. This study aims to introduce a novel approach by employing Grey Wolf Optimization (GWO) to determine the PI coefficients for brushless DC motor speed control, which is then implemented on an FPGA. During the study, a control strategy model for the BLDC motor was developed using MATLAB/Simulink. The motor’s speed was gradually increased from 300 to 600 and 900 rpm at specific intervals to calculate the controller coefficients. The GWO technique optimized the PI parameters, Kp and Ki, using the ITAE cost function. The results showed an improvement in speed control when comparing the conventional PI and GWO-PI controllers to the reference speed, with GWO-PI achieving closer adherence. As opposed to most studies that focus on simulations, this research tested the model using hardware, specifically the BASYS3 FPGA training card, demonstrating that the BLDC motor can operate at higher speeds in industrial settings with the optimized GWO-PI approach.