State and Unknown Inputs Estimations for Multi-Models Descriptor Systems

Abstract : In this note, the problem of states and unknown inputs estimation of nonlinear descriptor system is considered. The methodology is based on the use of Proportional Integral and Unknown Input Observers. The considered nonlinear descriptor system is transformed into an equivalent multi-models form by using the Takagi-Sugeno (T-S) approach. In this paper, the design methods of both proportional integral observers and unknown inputs observers for descriptor multi-models are described in detail. Sufficient conditions of stability analysis and gain matrices determination are per-formed by resolving a set of Linear Matrices Inequalities (LMIs). The design method offers all the degrees of design free-dom, which can be utilized to achieve various desired system specifications and performances and, thus, has great poten-tials in applications. A numerical example is employed to show the design procedure of these two observers and illustrate the effect of the proposed approach.
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American Journal of Computational and Applied Mathematics, 2012, 2 (3), p. 86-93, DOI: 10.5923/j.ajcam.20120203.04
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Chokri Mechmeche, Habib Hamdi, Mickael Rodrigues, Naceur Benhadj Braiek. State and Unknown Inputs Estimations for Multi-Models Descriptor Systems. American Journal of Computational and Applied Mathematics, 2012, 2 (3), p. 86-93, DOI: 10.5923/j.ajcam.20120203.04. 〈hal-00741162〉

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