Adaptive dynamic programming based fault compensation control for nonlinear systems with actuator failures

Abstract

This paper develops a novel fault compensation control scheme based on adaptive dynamic programming for nonlinear systems with actuator failures. The control scheme consists of a policy iteration algorithm and a fault compensation. For fault-free dynamic models, the Hamilton-Jacobi-Bellman equation is solved by policy iteration algorithm via constructing a critic neural network, and then the approximate optimal control policy can be derived directly. On the other hand, the online fault compensation is achieved without the fault detection and isolation mechanism by reconstructing the actuator failure. The closed-loop system is guaranteed to be asymptotically stable based on Lyapunov stability theorem. Two simulation examples are given to demonstrate the effectiveness of the present fault compensation control scheme.