Dynamic Model Based Incipient Fault Detection Concept for Robots

Abstract

The paper discusses two approaches to fault detection in robotic systems. The first approach consists of the use of a linear Luenberger state observer. This observer generates a robust residual (innovation) that is maximally sensitive to the faults that have to he detected while remaining unsensitive to the neglected non-linearities. It therefore serves as a fault indicator and allows a fault detection and unique fault isolat.ion. The computationally simple design and application procedure is outlined and illustrated by an example. The second part of the paper shows how a fully nonlinear observer for fault detection in robots can be designed and applied. Because of its non-linear observer structure the estimation error dynamics of this fault detection observer is linear and the choice of the observer feedback is particularily simple. Its residual is only a function of the faults and the model uncertainties. The fault detection abilities are significantly improved compared to the linear residual generation procedure. Simulation results show the successful implementation.