Nonlinear dynamic systems fault detection and isolation using fuzzy adaptive observer

A novel fault detection and isolation scheme for a class of nonlinear systems is presented in this paper using fuzzy adaptive observer. First, the study assumes the physical process can be described by Takagi-Sugeno (T-S) fuzzy model. In the light of the T-S fuzzy model of the plant, the fuzzy observer comprises a number of locally linear observers and the final state estimate is a fuzzy fusion of all local observer outputs. Moreover, a radial basis function (RBF) neural networks is designed for online fault estimation. The weights of neural Networks are adjusted by an adaptive algorithm which is established in the sense of Lyapunov stability theory to ensure the stability and convergence of the observer. Finally the simulation results illustrate the efficiency of the proposed method.