Cuckoo Optimization Algorithm Based Fuzzy Logic Speed Controller for BLDC Motor

Abstract

Brushless direct current (BLDC) motors are widely used in dynamic applications because of advantages such as high efficiency, wide speed range and low maintenance requirements. The classical Proportional-Integral (PI) control method is generally used for speed control of BLDC motor drivers. Although this method is easy to apply, the determined controller coefficients are generally constant and hence insufficient in dynamic changes. For this reason, methods that respond faster to dynamic changes, such as fuzzy-PI, have been proposed in the literature. Although the rule-based fuzzy controller increases its response ability to dynamic changes, determined rule-based coefficients affects the system performance completely. Therefore, the determination of the rule base values of the fuzzy controller is critical. In this paper, meta-heuristic Cuckoo Optimization Algorithm (COA) is proposed to determine the rule base values of the fuzzy controller for BLDC motor. Additionally, the rule-based table values of the fuzzy controller used for BLDC motor is determined using other meta-heuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Imperialist Competitive Algorithm (ICA), Invasive Weed Optimization (IWO) and the results are compared. Finally, experimental studies for Pittman44 series BLDC motor are also carried out and the results are obtained.