Performance Evaluation of a GWO-Optimized PID Controller for DC Motor System

Abstract

A classic control method and a modern optimization approach were combined in this research to better address industrial needs. A DC motor was chosen for its flexibility and ease of control, and a model was developed to assess performance under various conditions. Rather than relying on manual tuning, an advanced algorithm was used to adjust the controller settings, and accuracy was measured with a trusted indicator. With this approach, faster and more accurate results were achieved compared to traditional methods. Overall, the use of the Grey Wolf Optimization (GWO) approach not only made the system faster but also noticeably more accurate compared to previous methods. When compared to other optimization methods, including the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), further performance benefits were demonstrated. The response of the controller remained within the designated design parameters, with errors reduced to the lowest level. These results demonstrate that employing GWO with ITAE-based tuning offers a robust and efficient solution for industrial motor control, ensuring both rapid responsiveness and reliable stability.