Neural network based fault detection in robotic manipulators

Exponentially stable trajectory following of robotic manipulators under a class of adaptive controls. In this paper we investigate the problem of fault diagnosis in rigid-link robotic manipulators. A learning architecture, with neural networks as on-line approximators of the oo-nominal system behavior, is used for monitoring the robotic system for faults. The approximation of the oo-nominal behavior provides a model of the fault characteristics which is used for the detection of faults. The stability and performance properties of the proposed fault detection scheme in the presence of system failure are rigorously established. Simulation examples illustrate the ability of the neural network based fault diagnosis methodology described in this paper to detect and accommodate faults in a two-link robotic system. This paper presents an approach for designing nonlinear fault diagnosis algorithms. This methodology can be enhanced by combination with parameter estimation methods, parity equations and fuzzy approaches 2, 7].