Fault detection and isolation in cooperative manipulators via artificial neural networks

Abstract

When two or more robotic manipulators are working cooperatively, faults can put at risk the task, the robots, or the manipulated load. In this work, two artificial neural networks are employed in a fault detection and isolation system for cooperative robotic manipulators. A multilayer perceptron is utilized to reproduce the dynamics of the cooperative system The difference between its outputs and the actual velocity measurements generates the residual vector. This vector is classified by a radial basis function network that produces the fault information. Simulations with two robotic manipulators performing a cooperative task are presented, indicating that free-swinging joint faults are correctly detected and isolated. The main contribution of this work is the first application of fault detection and isolation to cooperative manipulators with faults at the robots' joints.