Journal of Intelligent and Robotic Systems
Fault Diagnosis Using Wavelet Neural Networks
Neural Processing Letters
Modelling and control of a complex system using a new approach
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
Fault detection and diagnosis based on modeling and estimation methods
IEEE Transactions on Neural Networks
Exogenous fault detection in a collective robotic task
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Dealing with fault dynamics in nonlinear systems via double neural network units
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Tracking control based on neural network for robot manipulator
TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
A robust fault detection and isolation scheme for robot manipulators based on neural networks
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
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Fault diagnosis plays an important role in the operation of modern robotic systems. A number of researchers have proposed fault diagnosis architectures for robotic manipulators using the model-based analytical redundancy approach. One of the key issues in the design of such fault diagnosis schemes is the effect of modeling uncertainties on their performance. This paper investigates the problem of fault diagnosis in rigid-link robotic manipulators with modeling uncertainties. A learning architecture with sigmoidal neural networks is used to monitor the robotic system for any off-nominal behavior due to faults. The robustness and stability properties of the fault diagnosis scheme are rigorously established. Simulation examples are presented to illustrate the ability of the neural-network-based robust fault diagnosis scheme to detect and accommodate faults in a two-link robotic manipulator