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Structurally robust control of complex networks.

Jose C Nacher1, Tatsuya Akutsu2

  • 1Department of Information Science, Faculty of Science, Toho University, Miyama 2-2-1, Funabashi, Chiba 274-8510, Japan.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|February 14, 2015
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Summary
This summary is machine-generated.

We introduce structurally robust control for complex networks, showing scale-free networks can be controlled with fewer controllers by adjusting minimum degree. This method accounts for link failures, like neural unreliability.

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Area of Science:

  • Control Theory
  • Network Science
  • Systems Engineering

Background:

  • Robust control theory is vital for real-world systems but struggles with unreliable components and structural changes in complex networks.
  • Existing methods do not adequately address scale-free topologies and probabilistic link failures in network control.
  • The need for advanced control strategies in complex, dynamic systems is increasing.

Purpose of the Study:

  • To introduce the concept of structurally robust control for complex networks.
  • To develop analytical tools and algorithmic frameworks for controlling networks with scale-free topologies.
  • To investigate the impact of adjusting the minimum degree on control robustness and controller order.

Main Methods:

  • Development of analytical tools for structurally robust control.
  • Application of an engineering algorithmic framework to complex networks.
  • Computer simulations and real network analyses, including scale-free topologies.
  • Investigation of probabilistic link failures using examples like Caenorhabditis elegans neural unreliability.

Main Results:

  • Structurally robust control is achievable in scale-free networks.
  • The same order of controllers as nonrobust configurations is sufficient by adjusting the minimum degree.
  • The methodology effectively addresses probabilistic link failures in real-world systems.
  • A new direction for control theory in complex network studies is suggested.

Conclusions:

  • Adjusting the minimum degree is a key factor in achieving robust control in scale-free networks.
  • The proposed methodology offers a novel approach to control engineering for complex and unreliable systems.
  • This work bridges robust control theory and network science, with implications for fields like neuroscience.