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Robust internal model design by nonlinear regression via low-power high-gain observers

Abstract

In this paper we introduce the low-power high-gain observer, developed in [1], to solve problems of output regulation for nonlinear systems. We show how the new tool makes it possible the implementation of high dimensional controllers, that tipically arise when the ideal steady-state control that must be generated to secure zero regulation error is affected by uncertainties.
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Dates and versions

hal-02304179 , version 1 (03-10-2019)

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Michelangelo Bin, Daniele Astolfi, Lorenzo Marconi. Robust internal model design by nonlinear regression via low-power high-gain observers. 2016 IEEE 55th Conference on Decision and Control (CDC), Dec 2016, Las Vegas, United States. pp.4740-4745, ⟨10.1109/CDC.2016.7798992⟩. ⟨hal-02304179⟩
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