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Unknown input estimation for nonlinear descriptor systems via LMIs and Takagi-Sugeno models

Abstract : This paper presents an unknown inputs observer for nonlinear descriptor systems. The approach uses the Takagi-Sugeno representation of the nonlinear model. In order to obtain strict linear matrix inequalities a novel observer structure is given. Thus the conditions can be efficiently solved via convex optimization techniques. A numerical example is provided to illustrate the performance of the proposed approach.
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https://hal-uphf.archives-ouvertes.fr/hal-03411796
Contributor : Mylène Delrue Connect in order to contact the contributor
Submitted on : Tuesday, November 2, 2021 - 3:28:47 PM
Last modification on : Monday, November 8, 2021 - 8:07:39 AM

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Víctor Estrada Manzo, Zsofia Lendek, Thierry-Marie Guerra. Unknown input estimation for nonlinear descriptor systems via LMIs and Takagi-Sugeno models. 2015 54th IEEE Conference on Decision and Control (CDC), Dec 2015, Osaka, Japan. pp.6349-6354, ⟨10.1109/CDC.2015.7403219⟩. ⟨hal-03411796⟩

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