Robust adaptive fault accommodation for a robot system using a radial basis function neural network

Abstract

A discrete-time radial basis function (RBF) neural network is designed for the fault accommodation of robotic systems. A robust learning algorithm using the adaptive dead-zone technique is presented to train the network parameters (weights and centres). This scheme assures the convergence of the estimate errors of both the neural network and the fault-monitoring system in the presence of system uncertainties. Simulations have been done on applying the RBF-network-based fault accommodation scheme to a two-link robotic manipulator. The main advantage of the adaptive algorithm is that the upper bound of system uncertainties is not known in advance, which makes the system more practical for the fault accommodation scheme as demonstrated.