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Mixed H∞/passive synchronization for complex dynamical networks with sampled-data control

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  • Su, Lei
  • Shen, Hao

Abstract

This paper deals with the problem of mixed H∞/passive synchronization for complex dynamical networks (CDNs) with time-varying delayed couplings via a sampled-data control scheme. The purpose is focus on designing controller such that the resulting synchronization error system is stable and a mixed H∞/passive performance level is satisfied. By using some new tools to deal with the Lyapunov functional, a sufficient condition which ensures the existence of the desired controller is presented. Based on the condition, an explicit expression for the desired controller is given. Finally, two examples are employed to demonstrate the effectiveness and the reduced conservatism of the proposed method.

Suggested Citation

  • Su, Lei & Shen, Hao, 2015. "Mixed H∞/passive synchronization for complex dynamical networks with sampled-data control," Applied Mathematics and Computation, Elsevier, vol. 259(C), pages 931-942.
  • Handle: RePEc:eee:apmaco:v:259:y:2015:i:c:p:931-942
    DOI: 10.1016/j.amc.2015.03.033
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    Cited by:

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    12. Kumar, S. Vimal & Anthoni, S. Marshal & Raja, R., 2019. "Dissipative analysis for aircraft flight control systems with randomly occurring uncertainties via non-fragile sampled-data control," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 217-226.
    13. Tan, Guoqiang & Wang, Zhanshan & Li, Cong, 2020. "H∞ performance state estimation of delayed static neural networks based on an improved proportional-integral estimator," Applied Mathematics and Computation, Elsevier, vol. 370(C).
    14. Liang, Kun & Dai, Mingcheng & Shen, Hao & Wang, Jing & Wang, Zhen & Chen, Bo, 2018. "L2−L∞ synchronization for singularly perturbed complex networks with semi-Markov jump topology," Applied Mathematics and Computation, Elsevier, vol. 321(C), pages 450-462.
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    17. Wang, Xiaomin & Li, Feng & Hu, Xingliu & Wang, Jing, 2023. "Mixed H∞/passive synchronization for persistent dwell-time switched neural networks via an activation function dividing method," Applied Mathematics and Computation, Elsevier, vol. 442(C).

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