H2 Fault Tolerant Controller Design for a Class of Nonlinear Systems with a Spacecraft Control Application

This paper presents an H2 robust fault tolerant controller design method for uncertain systems in the presence of unknown failures. The developed H2 controller with optimal index incorporates a neural network learning action and sliding mode control action. A radial basis function (RBF) neural network is utilized to approximate the unknown nonlinear dynamics. An updating rule is designed to estimate actuator failure. The sliding-mode control is used to eliminate the effect of neural network approximation error. Based on Lyapunov function, the sufficient condition for H2 optimal performance is developed in terms of nonlinear quadratic matrix inequality. In order to reduce computing cost, a simplification algorithm is developed, which avoids solving on-line nonlinear matrix inequality. A numerical example on a spacecraft system is presented to demonstrate the effectiveness of the proposed methods.