Neural network based fault detection in robotic manipulators

Abstract

Fault detection, diagnosis, and accommodation play a key role in the operation of autonomous and intelligent robotic systems. System faults, which typically result in changes in critical system parameters or even system dynamics, may lead to degradation in performance and unsafe operating: conditions. This paper investigates the problem of fault diagnosis in rigid-link robotic manipulators. A learning architecture, with neural networks as online approximators of the off-nominal system behaviour, is used for monitoring the robotic system for faults. The approximation (by the neural network) of the off-nominal behaviour provides a model of the fault characteristics which can be used for detection 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 illustrate the ability of the neural network based fault diagnosis methodology described in this paper to detect and accommodate faults in a simple two-link robotic system.