A neural-fuzzy sliding mode observer for robust fault diagnosis

A robust fault diagnosis (FD) scheme using Takagi-Sugeno (T-S) neural-fuzzy model and sliding mode technique is presented for a class of nonlinear systems that can be described by T-S fuzzy models. A neural-fuzzy observer and neural-fuzzy sliding mode observer are constructed respectively. A modified back-propagation (BP) algorithm is used to update the parameters of the two observers. Stability of the observers are analyzed as well. Finally, the proposed FD scheme using these observers is applied to a point mass satellite orbital control system example. Numerical simulation results show that this robust fault diagnosis strategy is effective for the considered class of nonlinear systems.