Abstract
This paper presents a sensor fault detection and isolation scheme for a class of Lipschitz nonlinear systems with nonlinear and unstructured modeling uncertainty. This significantly extends previous results by considering a more general class of system nonlinearities which are modeled as functions of the system input and partially measurable state variables. A new sensor fault diagnosis method is developed using adaptive estimation techniques. Adaptive thresholds for fault detection and isolation are rigorously derived. A simulation example of a single-link flexible joint robotic system is used to illustrate the effectiveness of the sensor fault diagnosis method.
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