Actuators fault diagnosis for robot manipulators with uncertain model

In this paper a fault diagnosis approach for robotic manipulators, subject to faults of the joints driving systems, is developed. A model-based diagnostic observer is adopted to detect, isolate and identify failures. Compensation of unknown dynamics, uncertainties and disturbances is achieved through the adoption of a class of neural interpolators, the support vector machines and trained off-line. Interpolation of unknown faults is performed by adopting an on-line neural interpolator based on radial basis functions, whose weights are adaptively tuned on-line. The effectiveness of the approach is experimentally tested on an industrial robot manipulator.