Fault-tolerant synthesis controller design for a flight-tracking system

This study presents a fault-tolerant synthesis controller design scheme for a class of non-linear systems with minimised performance index. A radial basis function (RBF) neural network with the online-updated centre and the width vector of Gaussian function is utilised to approximate the unknown non-linear dynamics. An updating rule is designed to estimate actuator failure. Based on estimated non-linear function and fault knowledge, a synthesis controller is developed with an optimal control action and a sliding-mode control action that is used to eliminate the effect of neural network approximation error. The sufficient condition for the optimal performance is given in terms of non-linear fault-dependent quadratic matrix inequality. Then, the solution of matrix inequality is reduced to solve off-line a fault-free Riccati equation. The feasibility of proposed method is demonstrated by a spacecraft model.