Robust Fault Diagnosis for a Satellite System Using a Neural Sliding Mode Observer

In this paper a nonlinear observer which synthesizes sliding mode techniques and neural state space models is proposed and is applied for robust fault diagnosis in a class of nonlinear systems. The sliding mode term is utilized to eliminate the effect of system uncertainties, and the switching gain is updated via an iterative learning algorithm. Moreover, the neural state space models are adopted to estimate state faults. Theoretically, the robustness, sensitivity, and stability of this neural sliding mode observer-based fault diagnosis scheme are rigorously investigated. Finally, the proposed robust fault diagnosis scheme is applied to a satellite dynamic system and simulation results illustrate its satisfactory performance.