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Parameters tracking identification based on finite-time synchronization for multi-links complex network via periodically switch control

Author

Listed:
  • Zhao, Hui
  • Li, Lixiang
  • Xiao, Jinghua
  • Yang, Yixian
  • Zheng, Mingwen

Abstract

In this paper, a class of periodically switch control method is proposed to achieve finite-time parameters tracking identification and synchronization for multi-link complex networks. This periodically switch control is an optimal control, two convertible control intensities are given in a fixed period instead of continuous high control intensity. Meanwhile, we give an effective analysis for complex network model with multiple constant time-delays and time-varying delays, we overcome these difficulties of time-delays and unknown parameters. The parameters estimation, topological identification are achieved based on parameters tracking identification of drive-response networks. Meanwhile, the corresponding identification and synchronization criteria are obtained based on Lyapunov function, linear matrix inequality (LMI) and finite-time stability theory. Finally, numerical simulations are given to verify the effectiveness of our proposed method.

Suggested Citation

  • Zhao, Hui & Li, Lixiang & Xiao, Jinghua & Yang, Yixian & Zheng, Mingwen, 2017. "Parameters tracking identification based on finite-time synchronization for multi-links complex network via periodically switch control," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 268-281.
    • Handle: RePEc:eee:chsofr:v:104:y:2017:i:c:p:268-281
    DOI: 10.1016/j.chaos.2017.08.024
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    References listed on IDEAS

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    1. Peng, Ya-Fu, 2009. "Robust intelligent sliding model control using recurrent cerebellar model articulation controller for uncertain nonlinear chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 39(1), pages 150-167.
    2. Wu, Jianshe & Jiao, Licheng, 2007. "Synchronization in complex delayed dynamical networks with nonsymmetric coupling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 386(1), pages 513-530.
    3. Hui Zhao & Lixiang Li & Haipeng Peng & Jürgen Kurths & Jinghua Xiao & Yixian Yang, 2015. "Anti-synchronization for stochastic memristor-based neural networks with non-modeled dynamics via adaptive control approach," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(5), pages 1-10, May.
    4. Wu, Jianshe & Jiao, Licheng, 2007. "Observer-based synchronization in complex dynamical networks with nonsymmetric coupling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 386(1), pages 469-480.
    5. Li, Lixiang & Li, Weiwei & Kurths, Jürgen & Luo, Qun & Yang, Yixian & Li, Shudong, 2015. "Pinning adaptive synchronization of a class of uncertain complex dynamical networks with multi-link against network deterioration," Chaos, Solitons & Fractals, Elsevier, vol. 72(C), pages 20-34.
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    Cited by:

    1. Dawei Ding & Ya Wang & Yongbing Hu & Zongli Yang & Hongwei Zhang & Xu Zhang, 2022. "Pinning synchronization and parameter identification of fractional-order complex-valued dynamical networks with multiple weights," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(8), pages 1-12, August.
    2. Sun, Yanqin & Wu, Huaiyu & Chen, Zhihuan & Zheng, Xiujuan & Chen, Yang, 2021. "Outer synchronization of two different multi-links complex networks by chattering-free control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).

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