Fault identification for robot manipulators
IEEE Transactions on Robotics
Automated fault diagnosis in nonlinear multivariable systems using a learning methodology
IEEE Transactions on Neural Networks
An efficient parameterization of dynamic neural networks for nonlinear system identification
IEEE Transactions on Neural Networks
A stable neural network-based observer with application to flexible-joint manipulators
IEEE Transactions on Neural Networks
Toward the training of feed-forward neural networks with the D-optimum input sequence
IEEE Transactions on Neural Networks
Wavelet Adaptive Backstepping Control for a Class of Nonlinear Systems
IEEE Transactions on Neural Networks
Support Vector Machines for Nonlinear Kernel ARMA System Identification
IEEE Transactions on Neural Networks
Multifeedback-Layer Neural Network
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Identification of Nonlinear Systems With Unknown Time Delay Based on Time-Delay Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Improving heat exchanger supervision using neural networks and rule based techniques
Expert Systems with Applications: An International Journal
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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. Magnetorquertype 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.