Performance Optimization of BLDC Motor Drives Using an Adaptive Elite Particle Swarm Optimization-Tuned Sliding Mode Controller

Abstract

A Brushless DC (BLDC) motor is a synchronous motor with a trapezoidal back electromotive force (EMF) waveform, which is typically operated with nonlinear control systems. This device depends on its speed controller to operate robustly. In most control applications, BLDC motor drives employ a proportional-integral-derivative (PID) controller to regulate speed; however, adjusting the controller's parameters is very challenging. Also, the PID controller may not be robust enough to maintain optimal motor performance when a disturbance occurs. Thus, a sliding mode controller (SMC) is proposed in this study, as it has been proven to achieve robust performance in the presence of external interference. This study presents a tuned SMC using an elite-based particle swarm optimization (EPSO) for the BLDC motor's control system. The aim is to determine the SMC’s parameters optimally, obtain a fast speed response without overshooting during its operation, and minimize the torque ripple. In this study, three performance indicators – integral time absolute error (ITAE), integral time squared error (ITSE), and integral squared error (ISE) are used to measure the effectiveness of optimizing the SMC. The results show that the lowest torque ripple and the best speed response curve are obtained when ITSE is used as the performance indicator. Finally, the proposed control system demonstrates superior performance considering external disturbances.