Comprehensive Study of Nonlinear Maglev System Utilizing COOT Optimized FOPID Controller

To improve the performance of the magnetic levitation system, the most recent metaheuristic COOT algorithm was first employed in this study to tune the Fractional Order Proportional Integral and Derivative (FOPID) controller. Because of its intrinsic instability and nonlinearity, the magnetic levitation system requires accurate and reliable control strategies to stay stable and reach the required position. Conventional PID controllers often fail to address the complexity of magnetic levitation systems due to their higher-order nature. The FOPID controller, with its fractional calculus-based design, offers greater flexibility and superior control performance by extending the integral and derivative orders to non-integer values. The objective function that encapsulates the magnetic levitation system’s performance requirements is minimized during the tuning phase. Integral Square Error (ISE) serves as the objective function in this case. The novelty of the proposed work is that the COOT algorithm is used to tune the FOPID controller by using the ISE as the objective function and examined by the servo and square responses in simulation and real-time. The contribution of the proposed work is two different algorithms, including the Genetic Algorithm and Whale Optimization Algorithm, were used to contrast the effectiveness of the FOPID controller based on the COOT method. In comparison to the other two algorithms, the simulation results and hardware results show that the FOPID controller tuned with the COOT algorithm performed substantially better. Additionally, the FOPID controller outperformed the PID controller tuned with the COOT algorithm.