Design of Controller for Brushless DC Motor Using a Hybrid Optimization Technique

Abstract

Brushless DC motors (BLDCs) are utilized in a diverse range of businesses and other contexts. BLDC motors have an advantage over other types of motors as a result of their exceptional power density as well as the ease with which they can be controlled. Using a hybrid optimization strategy, this paper presents the design of a PI controller that can be used for BLDC motors. The controller is designed using the Particle Swarm Optimization (PSO) technique in conjunction with fuzzy logic. The performance of the proposed control scheme is validated by comparing it with the PI controller that was designed using three other well-known optimization techniques, i.e., bacterial forging PSO (BF-PSO), ant colony optimization (ACO), and simulation annealing techniques are some of these methods (SA). MATLAB was used to successfully implement BLDC motor speed control using the controllers that were designed. A comparative analysis of the designed controllers was carried out based on performance indices, sensitivity, complementary sensitivity functions, time response, frequency response, and the results of simulations. It has been observed through comparative research that PI controllers based on PSO-Fuzzy have better performance.