NEURAL-NETWORK-BASED FAULT-DETECTION IN ROBOTIC MANIPULATORS

Fault detection, diagnosis, and accommodation play a key role in the o peration of autonomous and intelligent robotic systems. System faults, which typically result in changes in critical system parameters or ev en system dynamics, may lead to degradation in performance and unsafe operating conditions, This paper investigates the problem of fault dia gnosis in rigid-link robotic manipulators, A learning architecture, wi th neural networks as on-line approximators of the off-nominal system behavior, is used for monitoring the robotic system for faults. The ap proximation (by the neural network) of the off-nominal behavior provid es a model of the fault characteristics which can be used for detectio n and isolation of faults. The stability and performance properties of the proposed fault detection scheme in the presence of system failure are rigorously established. Simulation examples are presented to illu strate the ability of the neural network based fault diagnosis methodo logy described in this paper to detect and accommodate faults in a sim ple two-link robotic system.