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In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective

In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically meaningful, we consider restricted parameter perturbation by imposing two constraints: it must be experimentally realizable and applied only temporarily. We introduce the concept of attractor network, which allows us to formulate a quantifiable controllability framework for nonlinear dynamical networks: a network is more controllable if the attractor network is more strongly connected. We test our control framework using examples from various models of experimental gene regulatory networks and demonstrate the beneficial role of noise in facilitating control.

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    Title
    • A Geometrical Approach to Control and Controllability of Nonlinear Dynamical Networks
    Contributors
    Date Created
    2016-04-14
    Resource Type
  • Text
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    Identifier
    • Digital object identifier: 10.1038/ncomms11323
    • Identifier Type
      International standard serial number
      Identifier Value
      2041-1723
    Note
    • The final version of this article, as published in Nature Communications, can be viewed online at: https://www.nature.com/articles/ncomms11323

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    Wang, L., Su, R., Huang, Z., Wang, X., Wang, W., Grebogi, C., & Lai, Y. (2016). A geometrical approach to control and controllability of nonlinear dynamical networks. Nature Communications, 7, 11323. doi:10.1038/ncomms11323

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