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This study introduces a novel method for controlling complex networks using only local information. It addresses structural controllability and optimal control challenges in large-scale systems efficiently.

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Area of Science:

  • Complex systems science
  • Network theory
  • Control theory

Background:

  • Large-scale complex networks in various domains (social, biological, technological) present significant control challenges.
  • Existing methods for structural controllability and optimal control struggle with large networks lacking global topological information.
  • Controlling these systems requires guiding their dynamics and minimizing control costs.

Purpose of the Study:

  • To develop a unified approach for structural controllability and optimal control in large complex networks.
  • To overcome limitations of existing methods by utilizing only local network topology information.
  • To enable distributed control strategies for complex systems.

Main Methods:

  • Integration of graph theory and control theory principles.
  • A distributed local-game matching method for structural controllability using Bayesian games among adjacent nodes.
  • A minimizing longest control path method for optimal control, building upon structural controllability solutions.

Main Results:

  • A suboptimal solution for structural controllability is achieved with linear complexity.
  • Efficiently finding good solutions for optimal control in large networks.
  • Demonstrated linkage between structural controllability and optimal control problems.

Conclusions:

  • The proposed methods offer effective solutions for distributed control of complex networks.
  • This work provides a novel framework for addressing both structural controllability and optimal control simultaneously.
  • The approach is scalable and efficient for large-scale network analysis and management.