An intelligent sensor and actuator fault detection and isolation scheme for nonlinear systems

Abstract

This paper presents a robust fault detection and isolation (FDI) scheme for a general nonlinear system using a neural network-based observer. Both actuator and sensor faults are considered. The nonlinear system is subject to state and sensor uncertainties and disturbances. Two recurrent neural networks are employed to identify general unknown actuator and sensor faults. The neural network weights are updated according to a modified backpropagation scheme. Unlike many previous methods in the literature, the proposed FDI scheme does not rely on the availability of full state measurements. The stability of the overall fault detection scheme in presence of unknown sensor and actuator faults as well as plant and sensor uncertainties is shown by using Lyapunov's direct method. The stability analysis presented imposes no restrictive assumptions or constraints on the system and/or the FDI algorithm. Magnetorquer type actuators and magnetometer type sensors that are commonly utilized in the attitude determination and control of low-Earth orbit (LEO) satellites are considered as case studies. The effectiveness of our proposed fault diagnosis strategy is demonstrated through numerical simulations.