Genetic Algorithm Optimized High Precision Nonlinear Fractional Order PID Controller for Magnetic Levitation System

Conventional Proportional Integral Derivative (PID) controllers are widely used in industrial control systems because of their simplicity and effectiveness. However, controlling nonlinear and inherently unstable systems remains challenging with traditional PID controllers, as they often exhibit poor robustness, limited stability margins and unsatisfactory transient response characteristics in complex dynamic environments. To overcome these limitations, this work proposes the development and real time implementation of a Genetic Algorithm optimized Fractional Order PID (GAFOPID) controller. This controller extends the control action beyond integer order dynamics by utilizing fractional calculus for its integral and derivative components. The optimization of the FOPID controller’s parameters is achieved through a metaheuristic global optimization technique, specifically utilizing a genetic algorithm. The effectiveness of this controller is tested on the Quanser magnetic levitation (MagLev) system, which serves as a nonlinear actuated system for evaluation. To implement this control method, the Matlab Simulink library is employed.The performance of a GAFOPID controller is compared to that of a GA optimized traditional PID controller (GAPID). The results illustrate significant improvements in stability augmentation, reduced overshoot and enhanced response time across various reference trajectories. This research provides a robust framework for advanced control applications, allowing for efficient tuning that leads to offering superior adaptability and precision.