Genetic Algorithm-Based Optimization of PID Controller for Magnetic Levitation Systems: A Comparative Study

This paper proposes a PID controller design for Magnetic Levitation System (MLS), focusing on efficient parameter tuning using the Genetic Algorithm (GA). Additionally, the study examines four optimization techniques – Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Ziegler-Nichols (ZN) Method – for PID controller adjustment in maglev systems. The Magnetic Levitation System (MLS) provides a vital platform for control system analysis due to its nonlinear and unstable nature, relevant in applications like high-speed trains and frictionless bearings. Evaluations based on settling time, overshoot, and stability through simulations and experiments show that the GA outperforms all other techniques studied.