The effect of an evolutionary algorithm's rapid convergence on improving DC motor response using a PID controller

Abstract

Mobile robots experience a lot of challenges with path tracking because of the significant problem with DC motor speed control. The particle swarm optimization (PSO) tuned proportional integral derivative (PID) controller is commonly used to control DC motor speed. However, it fails to reach the optimal solution quickly. Therefore, this paper demonstrates the application of the artificial bee colony (ABC) algorithm to tune a PID controller for speed regulation of a DC motor. The proposed approach was used to obtain the optimal PID controller gains (proportional, integral, and derivative) by minimizing the integral of time absolute error (ITAE) as the objective function. The analysis of statistical tests, transients, and frequency responses was conducted to test the rapid convergence of the proposed approach. Also, the performance of the proposed ABC-tuned PID controller has been compared with three variants of the PSO-tuned PID controller. The conducted simulation results and comparison with the proposed ABC controller and variant PSO controllers have shown that the proposed ABC-PID controller is more effective and robust than the variant PSO-PID controllers for the speed control of a DC motor.