Authors:
Rodolfo Orjuela
;
Benoît Marx
;
José Ragot
and
Didier Maquin
Affiliation:
Centre de Recherche en Automatique de Nancy, UMR 7039, Nancy-Université, CNRS, France
Keyword(s):
State estimation, nonlinear discrete-time systems, multiple model approach, decoupled multiple model.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Nonlinear Signals and Systems
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
Multiple model approach is a powerful tool for modelling nonlinear systems. Two structures of multiple models can be distinguished. The first structure is characterised by decoupled submodels, i.e. with no common state (decoupled multiple model), in opposition to the second one where the submodels share the same state (Takagi-Sugeno multiple model). A wide number of research works investigate the state estimation of nonlinear systems represented by a classic Takagi-Sugeno multiple model. On the other hand, to our knowledge, the state estimation of the decoupled multiple model has not been investigated extensively. This paper deals with the state estimation of nonlinear systems represented by a decoupled multiple model. Conditions for ensuring the convergence of the estimation error are formulated in terms of a set of Linear Matrix Inequalities (LMIs) employing the Lyapunov direct method.