Fault-tolerant control for nonlinear time-delay systems using neural network observers

Abstract

This article addresses the problem of fault-tolerant control in nonlinear time-delay systems using adaptive dynamic programming. An adaptive neural network observer is developed to estimate unknown dynamics, system states, and actuator faults. This observer is then transformed into an augmented structure for optimal fault-tolerant control problem. The gains of this observer are determined by solving a linear matrix inequality. A new value function index is introduced to account for time-delay states, and control law is derived associated with this novel value function. The Hamilton–Jacobi–Bellman equation for this value function is solved via a critic neural network. Lyapunov functional analysis demonstrates that the closed-loop system remains uniformly ultimately bounded. Simulation results validate the proposed fault tolerant approach. The key contribution of this paper lies in incorporating time-delay states into the adaptive dynamic programming value function in the presence of actuator faults.