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.