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Analytical and neuro-fuzzy modeling for fault detection and identification for nonlinear systems: application to robot manipulator

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This work deals with modeling and fault detection and identification for robot manipulator. We have used for a dynamical system a hybrid approach. The model is decomposed into two parts: first, a certain part modeled using classical analytical theory and it is preferable to be linear. Second, an uncertain part representing the nonlinearities neglected in the first part, which is modeled using neuro-fuzzy modeling. Both analytical redundancy and neuro-fuzzy modeling are used to improve robustness. The analytical redundancy is used to generate residuals for the fault detection and location procedure. The neuro-fuzzy modeling is used to model modeling errors and faults, which allows performing the robustness and the sensitivity. Thanks to neuro-fuzzy modeling the errors of modeling are compensated and the faults are well identified as it is shown through the results of simulation.
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Bibliogr. 34 poz., rys.
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