DESIGN OF GENETIC ALGORITHM TUNED PID CONTROLLER FOR AIR GAP CONTROL OF A MAGNETIC LEVITATION SYSTEM
Magnetic levitation (maglev) systems offer high-speed, low-noise, and environmentally sustainable transportation solutions, but their performance critically depends on precise air gap control. This study presents the design of a Proportional-Integral-Derivative (PID) controller for a maglev train system, optimized using a Genetic Algorithm (GA). The system was modeled and simulated in MATLAB/Simulink, and controller performance was evaluated under reference tracking, disturbance rejection, and load variation scenarios. A comparative analysis was conducted against a conventional Ziegler�Nichols (ZN) tuned PID controller. Results show that the GA-tuned controller achieved a rise time of 0.0224s, settling time of 0.0631s, no overshoot, and steady-state error, outperforming the ZN-tuned controller which exhibited 13.74% overshoot and slower disturbance recovery despite a slightly faster rise time. Furthermore, the GA-tuned controller-maintained robustness under mass variation and rejected step disturbances within 0.023�0.050s, meeting design specifications. The findings demonstrate that GA optimization provides superior air gap regulation and robustness compared to conventional tuning methods, enhancing the reliability of maglev systems.
Authors : Adamu, A., Ibrahim, U., Jival, A. and Aminu, M.
Category : Open Access Volume (Issue) : 11(2) Date Uploaded : 31st August 2025