Fixed-Time Prescribed Performance Neural Fault-Tolerant Control for Uncertain Nonlinear Systems with Time-Varying Full-State Constraints

Abstract This paper addresses a fundamental challenge in nonlinear control: ensuring safety through state constraints and performance through prescribed tracking accuracy for uncertain systems with actuator faults, while guaranteeing practical fixed-time convergence independent of initial conditions. The primary contribution is a unified control framework that integrates four critical capabilities: fixed-time convergence with a priori known settling time to a residual set, strict enforcement of time-varying full-state constraints, prescribed performance tracking without requiring prior knowledge of the reference trajectory, and robustness to actuator faults and system uncertainties. Two key innovations enable these capabilities. First, a novel prescribed performance boundary is constructed using a dynamically scaled function that automatically adapts to the available state space, eliminating the conventional requirement for a priori reference knowledge. Second, newly designed tangent barrier Lyapunov functions with high-order stabilization terms rigorously ensure time-varying state constraints are never violated while enabling fixed-time stability analysis. Neural networks online compensate for system uncertainties and actuator faults. Lyapunov analysis proves all closed-loop signals remain bounded and converge to a residual set within a fixed time. Simulations on a second-order nonlinear system under time-varying constraints and a severe actuator fault demonstrate superior performance compared to existing methods, while strictly maintaining all constraints .