A robust fault detection and isolation scheme with application to magnetorquer type actuators for satellites

This paper presents a robust Fault Detection and Isolation (FDI) scheme for a general nonlinear system using a neural network based observer. The nonlinear system is subject to state and sensor uncertainties and disturbances. A recurrent nonlinear-in-parameters neural network (NLPNN) is employed to identify the general unknown fault. The neural network weights are updated based on a modified backpropagation scheme. Unlike many previous methods, the proposed fault detection and isolation scheme does not rely on the availability of all state measurements. The stability of the overall fault detection approach in the presence of unknown faults as well as plant and sensor uncertainties is shown using Lyapunov's direct method. Stability analysis presented here imposes no restrictive assumptions on the system and/or the FDI algorithm. Magnetorquer type actuators that are commonly utilized in the satellite attitude control system is considered as a case study. The effectiveness of the proposed fault diagnosis strategy is demonstrated via simulations.