A methodology for fault diagnosis in robotic systems using neural networks

Authors:
Arun T. Vemuri;Marios M. Polycarpou
Affiliations:
VLR Embedded, Inc., 411 E. Buckingham Rd. #636, Richardson, Texas 75081 (USA);Department of Electrical and Computer Engineering, University of Cincinnati, Cincinnati, Ohio 45221-0030 (USA).
Venue:
Robotica
Year:
2004

Citing 17
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Abstract

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 off-nominal behavior due to faults. The robustness, sensitivity, missed detection 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.