Sliding Mode Control for IT2 Fuzzy Systems Under Sensor Fault Estimation

This paper delves into the output feedback sliding mode control (SMC) problems in a class of Interval Type-2 (IT2) fuzzy systems affected by sensor faults. Firstly, a Markovian chain is employed to depict transmission channels vulnerable to stochastic deception attacks, providing a more accurate representation of the real-world scenario. Secondly, by leveraging historical estimates of fault signals, a novel learning-based observer integrated with a Saturation Function is proposed to reconstruct sensor faults. This observer incorporates a dynamic saturation level aimed at enhancing filtering performance. Subsequently, based on the estimated fault information, an output feedback SMC law is designed, and its finite-time boundedness is analyzed. Finally, the proposed approach and its associated theoretical findings are extended to an inverted pendulum model. Note to Practitioners—The proposed method introduces a novel sliding surface design based on real-time fault estimation and utilizes an adaptive saturation function to enhance system robustness. This approach proves especially effective when dealing with nonlinear, uncertain, and fault - prone systems across industrial automation, robotics, and aerospace engineering. Unlike conventional SMC methods, the proposed framework explicitly incorporates sensor fault dynamics, allowing for real-time compensation and ensuring finite-time boundedness of the system’s states. By implementing this control strategy, system performance can be improved, the impact of faults can be reduced, and operational stability can be enhanced, even in the presence of uncertainties and faults. This study provides practical insights and theoretical guarantees, bridging the gap between advanced control theory and its industrial applications.