IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v405y2014icp17-24.html
   My bibliography  Save this article

The control gain region for synchronization in non-diffusively coupled complex networks

Author

Listed:
  • Gequn, Liu
  • Wenhui, Li
  • Huijie, Yang
  • Knowles, Gareth

Abstract

The control gain region for synchronization of non-diffusively coupled networks was studied with respect to three conditions: synchronization, synchronization in finite time, and synchronization in the minimum time. Based on cancellation control methodology and master stability function formalism, we found that a complete feasible control gain region may be bounded, unbounded, empty or a union of several bounded and unbounded regions, with a similar shape to the synchronized region. An interesting possibility emerged that a network could be synchronized by both negative and positive feedback control simultaneously. By bridging synchronizability and synchronizing response speeds with a settling time index, we have developed timed synchronized region (TSR) as a substitute for the classical synchronized region to study finite time synchronization. As for the last condition, a graphical method was developed to estimate control gain with the minimum synchronization time (CGMST). Each condition has examples provided for illustration and verification.

Suggested Citation

  • Gequn, Liu & Wenhui, Li & Huijie, Yang & Knowles, Gareth, 2014. "The control gain region for synchronization in non-diffusively coupled complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 17-24.
  • Handle: RePEc:eee:phsmap:v:405:y:2014:i:c:p:17-24
    DOI: 10.1016/j.physa.2014.02.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437114001137
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2014.02.012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Chao & Duan, Zhisheng & Chen, Guanrong & Huang, Lin, 2007. "Analyzing and controlling the network synchronization regions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 386(1), pages 531-542.
    2. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2011. "Controllability of complex networks," Nature, Nature, vol. 473(7346), pages 167-173, May.
    3. Wang, Qingyun & Perc, Matjaž & Duan, Zhisheng & Chen, Guanrong, 2010. "Impact of delays and rewiring on the dynamics of small-world neuronal networks with two types of coupling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3299-3306.
    4. Wang, Xiao Fan & Chen, Guanrong, 2002. "Pinning control of scale-free dynamical networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 310(3), pages 521-531.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Sheng & Liu, Wenwen & Wu, Ruizi & Li, Junli, 2023. "An adaptive attack model to network controllability," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Li, Xin-Feng & Lu, Zhe-Ming, 2016. "Optimizing the controllability of arbitrary networks with genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 422-433.
    3. Ding, Jin & Lu, Yong-Zai & Chu, Jian, 2013. "Studies on controllability of directed networks with extremal optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(24), pages 6603-6615.
    4. Noah J Cowan & Erick J Chastain & Daril A Vilhena & James S Freudenberg & Carl T Bergstrom, 2012. "Nodal Dynamics, Not Degree Distributions, Determine the Structural Controllability of Complex Networks," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-5, June.
    5. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2012. "Control Centrality and Hierarchical Structure in Complex Networks," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-7, September.
    6. Yang Tang & Huijun Gao & Wei Zou & Jürgen Kurths, 2012. "Identifying Controlling Nodes in Neuronal Networks in Different Scales," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-13, July.
    7. Wang, Jiqiang, 2019. "Disturbance attenuation of complex dynamical systems through interaction topology design," Applied Mathematics and Computation, Elsevier, vol. 355(C), pages 576-584.
    8. Andreas Koulouris & Ioannis Katerelos & Theodore Tsekeris, 2013. "Multi-Equilibria Regulation Agent-Based Model of Opinion Dynamics in Social Networks," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 11(1), pages 51-70.
    9. He, He & Yang, Bo & Hu, Xiaoming, 2016. "Exploring community structure in networks by consensus dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 342-353.
    10. Wenle Zhang & Jianchang Liu, 2016. "Ultra-fast consensus of discrete-time multi-agent systems with multi-step predictive output feedback," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(6), pages 1465-1479, April.
    11. Ellinas, Christos & Allan, Neil & Johansson, Anders, 2016. "Project systemic risk: Application examples of a network model," International Journal of Production Economics, Elsevier, vol. 182(C), pages 50-62.
    12. Yang, Hyeonchae & Jung, Woo-Sung, 2016. "Structural efficiency to manipulate public research institution networks," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 21-32.
    13. Yu, Haitao & Wang, Jiang & Liu, Chen & Deng, Bin & Wei, Xile, 2014. "Delay-induced synchronization transitions in modular scale-free neuronal networks with hybrid electrical and chemical synapses," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 25-34.
    14. Miao, Qingying & Rong, Zhihai & Tang, Yang & Fang, Jianan, 2008. "Effects of degree correlation on the controllability of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(24), pages 6225-6230.
    15. Meng, Tao & Duan, Gaopeng & Li, Aming & Wang, Long, 2023. "Control energy scaling for target control of complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    16. Yunlong Wu & Qian Zhao & Hui Li, 2018. "Synchronization of directed complex networks with uncertainty and time-delay," International Journal of Distributed Sensor Networks, , vol. 14(5), pages 15501477187, May.
    17. Tao Jia & Robert F Spivey & Boleslaw Szymanski & Gyorgy Korniss, 2015. "An Analysis of the Matching Hypothesis in Networks," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-12, June.
    18. Yang, Xu-Hua & Lou, Shun-Li & Chen, Guang & Chen, Sheng-Yong & Huang, Wei, 2013. "Scale-free networks via attaching to random neighbors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3531-3536.
    19. Zhang, Rui & Wang, Xiaomeng & Cheng, Ming & Jia, Tao, 2019. "The evolution of network controllability in growing networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 257-266.
    20. Wouter Vermeer & Otto Koppius & Peter Vervest, 2018. "The Radiation-Transmission-Reception (RTR) model of propagation: Implications for the effectiveness of network interventions," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-21, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:405:y:2014:i:c:p:17-24. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.