Hybrid GWO-PSO Based Optimal PID Controller for Brushless PMDC Motor

This article presents an optimized design and tuning approach for Proportional-Integral-Derivative (PID) controllers applied to brushless Permanent Magnet Direct Current (PMDC) motors, widely used in industrial automation, robotics, and electric vehicles for their efficiency and precision. Traditional tuning methods like the Ziegler-Nichols (ZN) often fall short in handling the nonlinear and dynamic behavior of PMDC motors. To overcome these limitations, nature-inspired algorithms (NIAs) including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and a proposed hybrid GWO-PSO approach are utilized to enhance controller performance. The hybrid GWO-PSO algorithm combines the exploration strength of GWO with the exploitation capabilities of PSO, yielding superior optimization outcomes. A detailed PMDC motor model is developed in MATLAB/Simulink to assess each controller based on transient response, set-point tracking, disturbance rejection, and robustness. Simulation results indicate that the hybrid GWO-PSO-PID controller reduces rise time, overshoot, and settling time compared to the standard GA-PID, PSO-PID, and GWO-PID controller. It also shows better disturbance rejection and stability margins. These findings highlight the hybrid approach's effectiveness in improving control performance, offering a reliable solution for real-time PMDC motor applications.