A Recurrent Neural-Network-Based Sensor and Actuator Fault Detection and Isolation for Nonlinear Systems With Application to the Satellite's Attitude Control Subsystem

Abstract

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

Distributed fault diagnosis for process and sensor faults in a class of interconnected input–output nonlinear discrete-time systems

“…Based on the assumption that only a single fault occurs (either a process or a single sensor), adaptive approximation methods are used in order to build a fault detection estimator and suitable fault isolation estimators that correspond to the process and sensor faults that are able to determine which fault has occurred. In Talebi, Khorasani, and Tafazoli (2009) a recurrent neural-network based fault detection scheme for nonlinear systems is proposed, which employs two nonlinear-in-parameters neural networks to isolate actuator and sensor faults; the fault determined when the output of one of the neural networks produces a non-zero output indicating the faulty condition. In Thumati and Halligan (2013) a nonlinear observer-based fault diagnostics scheme, dealing with process and sensor faults, for nonlinear systems in discrete time is proposed.…”

“…In Thumati and Halligan (2013) a nonlinear observer-based fault diagnostics scheme, dealing with process and sensor faults, for nonlinear systems in discrete time is proposed. The scheme consists of an artificial immune system as an online approximator, which identifies the fault type by monitoring the outputs' magnitude of the two online approximators (state and output) as in Talebi et al (2009). In Q.…”

Fault Tolerant Attitude Control for Flexible Satellite with Uncertainties and Actuator Saturation

“…In 2011, Xiao [5] proposed a fault tolerant controller for flexible satellites without angular velocity magnitude measurement by ignoring the actuator dynamics. Moreover, numerous theories such as fuzzy control [6], second order sliding observer [7], diagnosis tree [8], recurrent Neural-Network [9], Kalman filter [10] and UKF [11], have been successfully employed in the fault diagnosis of the satellite system. More literature on fault diagnosis and fault tolerant control can be found in the reviews [12][13][14][15] and the references therein.…”

A Multiple-Observer Scheme for Fault Detection, Isolation and Recovery of Satellite Thrusters

“…Different model-based methods have been studied for satellite models: parity space [10], neural-network [8], parameter estimation techniques [4], observerbased techniques [1], bank of Kalman filters [12] and techniques based on unknown input observer (UIO) [6].…”