Intelligent Control Strategies for Magnetic Levitation System: Leveraging Type-2 Fuzzy CMAC and Elman Neural Networks

Magnetic levitation systems are known for their inherent nonlinearities and complexities, posing significant challenges to conventional control methods. This study aims to develop an advanced control strategy that addresses these challenges by utilizing type-2 fuzzy CMAC (Cerebellar Model Articulation Controller) and Elman neural networks. The motivation behind this research is to enhance the stability, accuracy, and robustness of magnetic levitation systems, which are crucial for precision applications. Our proposed hybrid approach leverages the adaptive capabilities of type-2 fuzzy logic to manage uncertainties and external disturbances, while the dynamic learning ability of the Elman neural network effectively captures the system’s behavior to provide robust feedback control. The type-2 fuzzy CMAC approximates the desired control actions, and the Elman neural network refines the control dynamics, resulting in a synergistic effect that significantly improves system performance. Experimental results demonstrate the superiority of the proposed method over traditional control techniques, highlighting improved stability and accuracy under varying conditions. This research provides a promising solution for the control of complex, nonlinear magnetic levitation systems and suggests potential for broader applications in advanced control engineering.