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Non-Fragile Optimal Guaranteed Cost Control for Descriptor T-S Fuzzy System

Wei Du

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Abstract

By the parallel distributed compensation(PDC) approach,the non-fragile optimal guaranteed cost controller is designed in the presence of the additive controller gain perturbations,which can ensure the closed-loop system asymptotic stability and make the upper bound of the cloop-loop performance minimized.Then,a sufficient condion for the existence of the controller is derived based on linear matrix inequalities(LMI).The design of controller is formulated as a convex optimization problem,which can be solved by the existing convex optimization techniques.Finally,an example is given to illustrate the effectiveness of the proposed method.

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What this paper is about

By the parallel distributed compensation(PDC) approach,the non-fragile optimal guaranteed cost controller is designed in the presence of the additive controller gain perturbations,which can ensure the closed-loop system asymptotic stability and make the upper bound of the cloop-loop performance minimized.Then,a sufficient condion for the existence of the controller is derived based on linear matrix inequalities(LMI).The design of controller is formulated as a convex optimization problem,which can be solved by the existing convex optimization techniques.Finally,an example is given to illustrate the effectiveness of the proposed method.

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Available abstract

By the parallel distributed compensation(PDC) approach,the non-fragile optimal guaranteed cost controller is designed in the presence of the additive controller gain perturbations,which can ensure the closed-loop system asymptotic stability and make the upper bound of the cloop-loop performance minimized.Then,a sufficient condion for the existence of the controller is derived based on linear matrix inequalities(LMI).The design of controller is formulated as a convex optimization problem,which can be solved by the existing convex optimization techniques.Finally,an example is given to illustrate the effectiveness of the proposed method.

Key concepts: Linear matrix inequality, Control theory (sociology), Convex optimization, Controller (irrigation), Upper and lower bounds, Mathematical optimization, Compensation (psychology), Fuzzy logic

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