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

Víctor Estrada Manzo , Zsofia Lendek (1) , Thierry-Marie Guerra (2)

1 Technical University of Cluj-Napoca
2 LAMIH - Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201

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.

Mots clés: "Observers"; "Mathematical model"; "Takagi-Sugeno model"; "Standards"; "Linear matrix inequalities"; "Numerical models"

Domaines: Automatique / Robotique

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⟩