Adaptive neural fault-tolerant optimal control for nonlinear uncertain systems with dynamic state constraints

This article investigates the issue of adaptive neural optimal fault-tolerant control for a class of nonlinear uncertain systems subject to dynamic state constraints and external disturbances. To handle more general dynamic constraints, a unified tangent-type nonlinear mapping is first proposed to transform the state-constrained system into one free of constraints. To solve the problem of actuator faults and external disturbances, a single network adaptive dynamic program method is designed, which consists of a feed-forward fault-tolerant control scheme and a feedback differential game control strategy. Neural networks are employed to approximate the uncertainties and cost function, respectively. To handle the issue of "explosion of complexity," a finite-time convergent differentiator is established to estimate the derivative of virtual control signals in the backstepping design. Via Lyapunov stability analysis, asymptotic stability of the original and transformed nonlinear system is theoretically guaranteed. Two comparative simulation examples are provided to evaluate the efficacy of the proposed control approach.