Optimal Control of Brushless DC Motor Using Soft Computing Optimization Techniques: A Review

Abstract

Brushless Direct Current (BLDC) motors are widely utilized in various applications due to their high efficiency, low maintenance, and robust performance. Achieving optimal control of BLDC motors, however, is a complex task due to their nonlinear dynamics, parameter uncertainties, and operating conditions. Traditional control strategies like PID often fail to deliver the desired performance across all operating conditions. Soft computing optimization techniques, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Fuzzy Logic (FL), Artificial Neural Networks (ANN), and Ant Colony Optimization (ACO), have been proposed to address these challenges. This paper presents a comprehensive review of the application of soft computing techniques for the optimal control of BLDC motors. We provide a detailed analysis of various optimization methods, their advantages, limitations, and specific applications in BLDC motor control. The review highlights the significant improvements in performance achieved through these methods and offers insight into future research directions.