Robust Fault Detection and Estimation for Descriptor Systems Based on Multi-Models Concept

Habib Hamdi (1) , Mickael Rodrigues (2) , Chokri Mechmeche (1) , Naceur Benhadj Braiek (1)

1 INSAT - Institut National des Sciences Appliquées et de Technologie [Tunis]
2 LAGEP - Laboratoire d'automatique et de génie des procédés

This paper addresses the robust fault detection and estimation problem of nonlinear descriptor system with unknown inputs observers. The considered nonlinear descriptor system is transformed into an equivalent multi-models form by using the Takagi-Sugeno (T-S) approach. Two cases are considered: the first one deals with the multi-models based on measurable decision variables and the second one assumes that these decision variables are unmeasurable. Then, a residual generator based on an unknown observer is designed for both fault detection and estimation. Stability analysis and gain matrices determination are performed by resolving a set of Linear Matrices Inequalities (LMIs) for both cases. The performances of the proposed fault detection and estimation method is successfully applied to an electrical circuit.

Mots clés: Descriptor multi-models; fault detection and estimation; Linear Matrices Inequalities (LMIs); residual generation

Domaines: Automatique / Robotique

Habib Hamdi, Mickael Rodrigues, Chokri Mechmeche, Naceur Benhadj Braiek. Robust Fault Detection and Estimation for Descriptor Systems Based on Multi-Models Concept. International Journal of Control, Automation and Systems, 2012, 10 (6), pp.1260-1266. ⟨10.1007/s12555-012-0622-z⟩. ⟨hal-00752421⟩