AN APPROACH TO NONLINEAR ROBUST FAULT DIAGNOSIS BASED ON NEURAL NETWORK ON-LINE APPROXIMATOR

A fault detection method based on neural networks on-line approximation structure for uncertain nonlinear system is presented in this paper. A neural network approximator is used for learning the nonlinear fault functions to monitor the abnormal behavior of dynamic system. When system faults occur, the on-line learning structure can approximate all possible unknown faults, then the faults are identified and accommodated. The uniformly ultimately bounded stability of closed-loop error system is guaranteed by Lya-punov stability theory and the weights is tuning without need of persistency of excitation.