Abstract
Brushlike Direct Current Motors(BLDCM) are the most extensively used machine in a wide range of oceanic applications such as operation of offshore wind turbines, including robotics, food technology, and aviation. PID controllers exceed other linear controllers in terms of performance. This controller is typically utilized for controlling the motor’s speed. In computing, the traditional approach for adjusting PID parameters is indirect. In this paper, two non-traditional algorithms, Genetic Algorithm and Ant Colony Optimization, are proposed for tuning PID parameters in order to control the speed of BLDC motor. With the goal of constructing a speed regulation controller, these algorithms were applied and assessed on a second-order plant model of a BLDC motor. The GA- and PSO-based control algorithms were implemented using MATLAB–Simulink interfaces. For each technique, the resulting system performance was compared.