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Intermittent control on switched networks via \({\varvec{\omega }}\)-matrix measure method

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Abstract

This paper is concerned with global exponential synchronization problem for a class of switched delay networks with interval parameters uncertainty, different from the most existing results, without constructing complex Lyapunov–Krasovskii functions; \(\omega \)-matrix measure method is firstly introduced to switched interval networks, combining Halanay inequality technique, designing proper intermittent and non-intermittent control strategy; some easy-to-verify synchronization criteria are given to ensure the global exponential synchronization of switched interval networks under arbitrary switching rule and for admissible interval uncertainties. Moreover, as an application, the proposed scheme can be applied to chaotic neural networks. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical results and show the obtained results via employing \(\omega \)-measure are superior to previous results by using \(1\)-measure.

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Acknowledgments

This work was jointly supported by the National Natural Science Foundation of China (NSFC) under Grants Nos. 61272530 and 11072059, and the Natural Science Foundation of Jiangsu Province of China under Grants No. BK2012741, and supported by the “Fundamental Research Funds for the Central Universities”, the JSPS Innovation Program under Grant \(\mathrm{CXLX13}\_\mathrm{075}\), and the Scientific Research Foundation of Graduate School of Southeast University YBJJ1407.

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Authors and Affiliations

  1. Department of Mathematics, Southeast University, Nanjing , 210096, China

    Ning Li

  2. Department of Mathematics, Southeast University, Nanjing , 210096, China

    Jinde Cao

  3. Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah , 21589, Saudi Arabia

    Jinde Cao

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  1. Ning Li
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  2. Jinde Cao
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Correspondence to Jinde Cao.

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Li, N., Cao, J. Intermittent control on switched networks via \({\varvec{\omega }}\)-matrix measure method. Nonlinear Dyn 77, 1363–1375 (2014). https://doi.org/10.1007/s11071-014-1385-2

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  • DOI: https://doi.org/10.1007/s11071-014-1385-2

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