Adaptive fuzzy control for full-state constrained stochastic nonlinear systems with dead zone and multiple faults

Abstract

This article investigates adaptive fuzzy control for stochastic nonlinear systems subject to multiple practical imperfections, including input dead-zone nonlinearities, time-varying sensor faults, state-dependent actuator faults, and full-state constraints. A novel adaptive fuzzy control scheme is developed via a recursive backstepping framework incorporating Barrier Lyapunov Functions at each design step to ensure that system states remain within predefined bounds despite stochastic disturbances. A state-dependent actuator fault model is introduced to capture realistic fault characteristics. Lyapunov-based analysis rigorously establishes that all closed-loop signals are semi-globally uniformly ultimately bounded, guaranteeing stability and constraint satisfaction. The effectiveness of the proposed approach is demonstrated through simulation studies on a numerical example and a robotic manipulator system, showing reliable tracking performance. Comparative results indicate improved handling of stochastic disturbances and dead-zone nonlinearities under concurrent fault conditions with state constraints.