Description: Fault diagnosis plays an important role in the oper-
ation 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. Platform: |
Size: 306176 |
Author:JTNT |
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