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Improved stabilization criteria for fuzzy systems under variable sampling
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DOI：10.1016/j.jfranklin.2017.07.024.png)
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Abstract
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This paper investigates the problem of stabilization for fuzzy sampled-data systems with variable sampling. A novel Lyapunov-Krasovskii functional (LKF) is introduced to the fuzzy systems. The benefit of the new approach is that the LKF develops more information about actual sampling pattern of the fuzzy sampled-data systems. In addition, some symmetric matrices involved in the LKF are not required to be positive definite. Based on a recently introduced Wirtinger-based integral inequality that has been shown to be less conservative than Jensen's inequality, much less conservative stabilization conditions are obtained. Then, the corresponding sampled-data controller can be synthesized by solving a set of linear matrix inequalities (LMIs). Finally, an illustrative example is given to show the feasibility and effectiveness of the proposed method. (C) 2017TheFranklinInstitute. PublishedbyElsevierLtd. Allrightsreserved.
Keywords:
TIME-VARYING DELAY
CHAOTIC LURE SYSTEMS
H-INFINITY CONTROL
LINEAR-SYSTEMS
INTEGRAL INEQUALITY
STABILITY-CRITERIA
NONLINEAR-SYSTEMS
MODEL
SYNCHRONIZATION
NETWORKS
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