Robust fault detection and estimation for descriptor systems based on multi-models concept.

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Abstract	Abstract: This paper addresses the robust fault detection and estimation problem of nonlin-ear 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 with 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 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.
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Subject Keyword	Robust Fault Detection Descriptor System Multi-models Concept Fault Detection Unknown Observer Linear Matrix Inequality Estimation Method Equivalent Multi-models Form Estimation Problem Nonlinear Descriptor System Residual Generator Unknown Input Observer Gain Matrix Determination Decision Variable Stability Analysis Electrical Circuit Nonlin-ear Descriptor System Measurable Decision Variable
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Resource Type	Article