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Adaptive state observers design using dynamic regressor extension and mixing

Abstract : In this paper we propose an adaptive state observer for a class of nonlinear systems with unknown parameters. Assuming that the system variables satisfy an algebraic constraint, a linear regression involving unknown parameters and unmeasurable states is established. The design proceeds adopting a gradient descent approach to minimize a quadratic criterion that is suggested by this regression. To separate the problems of state and parameter estimation we use the recently proposed dynamic regressor extension and mixing approach, whose main feature is that it allows to generate, out of an N-dimensional, linear vector regression, N scalar regression models. Moreover, under a weak interval excitation assumption, it ensures finite-time parameter convergence. It is shown that the proposed observer is applicable to several practically interesting physical systems.
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https://hal-centralesupelec.archives-ouvertes.fr/hal-02281777
Contributor : Stanislav Aranovskiy <>
Submitted on : Monday, September 9, 2019 - 2:52:55 PM
Last modification on : Monday, October 5, 2020 - 9:50:29 AM

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Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 International License

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Anton Pyrkin, Alexey Bobtsov, Romeo Ortega, Alexey Vedyakov, Stanislav Aranovskiy. Adaptive state observers design using dynamic regressor extension and mixing. Systems and Control Letters, Elsevier, 2019, 133, pp.104519. ⟨10.1016/j.sysconle.2019.104519⟩. ⟨hal-02281777⟩

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