Control of the BLDC Motor Using Ant Colony Optimization Algorithm for Tuning PID Parameters.

A key component of industrial applications is the direct current (DC) motor. Hence, due to their superior features and performance, brushless DC (BLDC) motors are more suitable for fractional kilowatt motor's applications. Albeit, for the purpose of controlling the speed of BLDC motor easily, it is quite difficult for obtaining the best controlling performance through the use of the conventional approaches of tuning. In order to search the proportional--integral--derivative (PID) tuning parameters optimally for the different controllers taken into consideration, the use of modern bio-inspired metaheuristic technique called ant colony optimization (ACO) algorithm is employed. This paper particularly discusses and presents on the tuning parameters of k_p; k_i; and k_d. The performance of traditional controller and novel approach is compared, analyzed, and presented. BLDC motor is buildup in MATLAB, and the usage, importance, efficiency, and strength of the proposed approach are validated against traditional tuning methods. The obtained result shows better performance of the proposed system with the aid of the proposed controllers for different speed trajectories of the drive when compared with that of the classical PID controllers. ACO seems to be one of the most effective tuning techniques of PID controllers. This research has significant impact on modern control applications. [ABSTRACT FROM AUTHOR]