An approach for the state estimation of Takagi-Sugeno models and application to sensor fault diagnosis

Dalil Ichalal (1) , Benoît Marx (1) , José Ragot (1) , Didier Maquin (1)

1 CRAN - Centre de Recherche en Automatique de Nancy

In this paper, a new method to design an observer for nonlinear systems described by Takagi-Sugeno (TS) model, with unmeasurable premise variables, is proposed. Most of existing work on TS models consider models with measurable decision variables. As a consequence, these works cannot be applied when the decision variables are not available to measurement. The idea of the proposed approach is to re- write the TS model with unmeasurable premise variable into an uncertain TS model by introducing the estimated state in the model. The convergence of the state estimation error is studied using the Lyapunov theory and the stability conditions are given in terms of Linear Matrix Inequalities (LMIs). Finally, an academic example is given to illustrate the proposed approach, with an application to sensor fault detection and isolation using an observer bank.

Mots clés: Nonlinear systems: Takagi-Sugeno models; state estimation; unmeasurable premise variable; uncertain systems; L2 optimization; sensor fault diagnosis

Domaines: Automatique / Robotique

Dalil Ichalal, Benoît Marx, José Ragot, Didier Maquin. An approach for the state estimation of Takagi-Sugeno models and application to sensor fault diagnosis. 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, Dec 2009, Shanghai, China. pp.7789-7794, ⟨10.1109/CDC.2009.5400028⟩. ⟨hal-00407198⟩