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Article Dans Une Revue Neurocomputing Année : 2019

Fault diagnosis observer for descriptor Takagi-Sugeno systems

Résumé

This paper proposes a methodology to design a robust observer for Takagi–Sugeno descriptor (TS-D) systems with unmeasurable premise variables and its application to sensor fault detection and isolation. A robust H∞ approach is considered to minimize the effect of uncertainties given by the unmeasurable premise variables, disturbances and sensor noise. As a result, a set of relaxed linear matrix inequalities (LMI) are derived, which provides sufficient conditions to guarantee the convergence of the proposed state observer. Finally, a conventional fault detection scheme is considered by means of residual generation and evaluation. An academic example is given to illustrate the effectiveness of the proposed method.
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Dates et versions

hal-01949129 , version 1 (09-12-2018)

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Francisco Ronay López-Estrada, Didier Theilliol, Carlos Manuel Astorga-Zaragoza, Jean-Christophe Ponsart, Guillermo Valencia-Palomo, et al.. Fault diagnosis observer for descriptor Takagi-Sugeno systems. Neurocomputing, 2019, 331, pp.10-17. ⟨10.1016/j.neucom.2018.11.055⟩. ⟨hal-01949129⟩
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