Fault Identification for a Class of Nonlinear Uncertain Systems Using High Gain Observer and Deterministic Learning ^*

In this article, by exploiting a high gain learning observer, a fault identification scheme is proposed for a class of nonlinear systems. First, by utilizing techniques of high gain adaptive observer and local approximator, a learning based high gain observer is designed, in which the state estimation and fault identification are implemented simultaneously. Based on the Lyapunov-based stability theorem, both of the state estimation errors and parameter errors can be proved to be uniformly ultimately bounded. Second, through using deterministic learning, a partial persistent excitation condition can be satisfied. Then, the interconnected system can be proved to be input-to-state stable by using the small gain theorem. This implies that local approximator can be guaranteed to compensate the unknown dynamics, and the observer gain can be effectively reduced. Simulation results show the effectiveness of the proposed fault identification scheme.