Optimization of Exact Feedback Linearization EKF Controller for Magnetic Levitation Using Cuckoo Search Algorithm

<ns3:p>Background Magnetic levitation is an electromechanical technique that allows objects to float in air using magnetic fields alone. This technology eliminates physical contact, thereby reducing friction, wear, and energy loss while providing high-precision and stable operation. A key challenge in maglev systems lies in their inherent instability and nonlinearity, which complicate control design. In this study, a hollow metal sphere is levitated as part of a real-time experimental setup, which requires a fine balance between gravitational and magnetic forces. Method To address the complexities of the Magnetic levitation system, an intelligent control strategy is employed. The core control architecture integrates an Extended Kalman Filter for accurate state estimation with Exact Feedback Linearization to manage the nonlinear dynamics of the system. Additionally, system performance is optimized through Cuckoo Search, a nature-inspired metaheuristic algorithm used for pole placement tuning within the feedback linearization framework. The Extended Kalman Filter estimates hidden states and compensates for noise and disturbances, while the Exact Feedback Linearization simplifies the system dynamics to a linear form for more straightforward controller design. The Cuckoo Search algorithm ensures optimal controller parameters by minimizing a performance index. Result The combined Cuckoo Search-optimized, Exact Feedback Linearization with Extended Kalman Filter demonstrates significant improvements in the robustness and stability of the Magnetic levitation system. Simulation and experimental results show enhanced tracking performance, faster settling time, and improved disturbance rejection compared to conventional methods. The system remains stable and responsive under various operating conditions, validating the effectiveness of the proposed approach. Conclusion The study presents a robust and intelligent control framework for magnetic levitation systems. The method effectively addresses the challenges posed by system nonlinearity and uncertainty, leading to improved control accuracy and system performance. These findings suggest that such intelligent control strategies are well-suited for practical applications of Magnetic levitation systems, will enhance reliability.</ns3:p>