Adaptive Output Feedback Control for Networked Nonlinear Systems With Hybrid Attacks: A Fuzzy Information Learning Approach

This article investigates adaptive output feedback fuzzy control for networked nonlinear systems with hybrid attacks using fuzzy information learning approach. First, the systems with external disturbances and parameter uncertainties are modeled via a fuzzy framework, and an improved hybrid attack model is developed by incorporating aperiodic DoS and FDI attacks. The adaptive event-triggered (ET) output feedback controller is proposed to handle unmeasured states and resource constraints. Then, to capture fuzzy information, a piecewise fuzzy Lyapunov–Krasovskii functional incorporating membership functions (MFs) is meticulously constructed using delayed fuzzy system theory. The fuzzy information learning algorithm is further designed to determine and exploit the actual lower and upper bounds of MF’ derivatives, resulting in an effective controller with enhanced control performance and resource efficiency. Eventually, the ET strategy parameters, controller gains, and the optimal performance index are derived within a unified framework, ensuring global exponential stability and guaranteed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i><sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance. Finally, the merits and applicability of the proposed approach are exemplified by a practical example.