Design of RBF-Based Adaptive Gain Fuzzy Sliding Mode Control for Uncertain Ball and Beam System

Abstract

This study explores the application of sliding mode control to the ball and beam system, a widely studied subject. Sliding mode control, known for its robustness, simplicity, and adaptability, has been extended through combinations with techniques like radial basis function (RBF) networks and fuzzy logic. The primary focus here is enhancing the reaching phase in sliding mode control using RBF, integrating a power rate reaching law, and fuzzy logic to optimize controller performance. Adapting the controller for real-world scenarios requires precise knowledge of the ball and beam system parameters, achieved through an integral-based approach to parameter identification. The proposed controller is then implemented, with a comprehensive comparison conducted among various techniques based on their step response characteristics. Results show that the proposed RBF-based adaptive fuzzy sliding mode controller yields a 250% transient time decrease compared to the power-reaching law SMC and a tenfold decrease in overshoots.