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Adaptive Estimation for Uncertain Nonlinear Systems: A Sliding-Mode Observer Approach

Abstract : This paper deals with the problem of adaptive estimation, i.e. the simultaneous estimation of the state and parameters, for a class of uncertain nonlinear systems. A nonlinear adaptive sliding-mode observer is proposed based on a nonlinear parameter estimation algorithm. The nonlinear parameter estimation algorithm provides a rate of convergence faster than exponential while the sliding-mode observer ensures ultimate boundness for the state estimation error attenuating the effects of the external disturbances. Linear matrix inequalities (LMIs) are provided for the synthesis of the adaptive observer and some simulation results show the feasibility of the proposed approach.
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https://hal.inria.fr/hal-01888534
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Submitted on : Friday, October 5, 2018 - 10:46:24 AM
Last modification on : Tuesday, November 24, 2020 - 2:18:23 PM
Long-term archiving on: : Sunday, January 6, 2019 - 1:40:08 PM

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Robert Franco, Hector Ríos, Denis Efimov, Wilfrid Perruquetti. Adaptive Estimation for Uncertain Nonlinear Systems: A Sliding-Mode Observer Approach. CDC 2018 - 57th IEEE Conference on Decision and Control, Dec 2018, Fontainebleau (FL), United States. ⟨hal-01888534⟩

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