Optimization of PID Controller for Brushless DC Motor Based on Dung Beetle Algorithm

Abstract

The optimization of a PID controller for a Brushless DC (BLDC) motor is a crucial task to enhance its performance in various applications, including robotics, electric vehicles, and industrial systems. Traditional PID tuning methods often rely on manual or heuristic approaches, which may not yield the best performance regarding stability, response time, and robustness. This paper proposes the use of the Dung Beetle Optimizer Algorithm (DBO), a bio-inspired optimization method, to optimize the PID controller parameters for a BLDC motor. The Dung Beetle Algorithm mimics the foraging behavior of dung beetles, utilizing a population-based search strategy to explore the solution space efficiently. By optimizing the PID controller's proportional, integral, and derivative gains, the DBA seeks to minimize the error between the desired and actual motor performance, improving dynamic response, reducing overshoot, and enhancing system stability. In this paper, we aim to integrate the Dung Beetle algorithm (DBO) to tune the PID controller for a Brushless DC (BLDC) motor’s speed control using Matlab simulation based on the objective function, which is the Integral Time Absolute Error (ITAE).  A comparison of optimal performances (rise time, settling time, overshoot, peak response, and peak time)  with a work that applied optimization by Genetic Algorithm and Simulated annealing on the same model and parameters of a Brushless DC motor. The results demonstrate that the DBO is the most effective approach to performance and convergence