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

Abstract

A key component of industrial applications is the 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 Brushless DC 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 tunning parameters of . The performance of traditional controller and novel approach is compared, analyzed and presented. Brushless DC motor is buildup in MATLAB and the usage, importance, efficiency and strength of the proposed approach is validated against traditional tunning 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 Proportional – Integral - Derivative (PID) controllers. Ant Colony Optimization (ACO) seems to be the one of the most effective tuning techniques of Proportional – Integral - Derivative (PID) controllers. This research has significant impact on modern control applications.