Comparing Metaheuristic Algorithms for PID Control of a DC Motor

This paper compares the performance of metaheuristic optimization algorithms for tuning a PID controller to control the speed of a DC motor. The Harris Hawk, Grey Wolf, Ant Lion, Artificial Bee Colony, and Energy Valley algorithms were implemented in MATLAB and tested on a DC motor model. Results show the algorithms have similar behavior, but Energy Valley gave minimum ITAE error at low iterations. For higher iterations, Grey Wolf and Ant Lion performed best in terms of ITAE error and rise time respectively. Ultimately, the findings indicate relatively minor distinctions between the techniques, suggesting they provide equivalent capabilities for PID optimization without a decisively superior method.