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Data-driven control of complex networks.

Giacomo Baggio1, Danielle S Bassett2,3,4,5,6,7, Fabio Pasqualetti8

  • 1Department of Information Engineering, University of Padova, Padova, Italy.

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Summary
This summary is machine-generated.

This study introduces a data-driven framework for optimal control of complex networks without needing prior knowledge of their dynamics. The method uses limited data to design effective control strategies for linear and nonlinear systems.

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Area of Science:

  • Control theory
  • Network science
  • Systems engineering

Background:

  • Controlling complex networks requires accurate models of their dynamics, which are often unavailable in practice.
  • Advances in control algorithms for network systems have been significant, but model dependency remains a challenge.

Purpose of the Study:

  • To develop a data-driven framework for optimal control of complex networks without prior knowledge of their dynamics.
  • To construct optimal controls using a finite set of data from network stimulation.

Main Methods:

  • A data-driven framework is developed to derive optimal control strategies.
  • The method utilizes arbitrary or random inputs to stimulate the unknown network dynamics.
  • Controls are constructed from a finite dataset obtained through network stimulation.

Main Results:

  • Optimal controls are derived for complex networks using only observational data.
  • The framework is proven effective for networks with linear dynamics.
  • Performance is characterized for noisy data and nonlinear dynamics, relevant to power grids and brain networks.

Conclusions:

  • A novel data-driven approach enables optimal control of complex networks without explicit dynamic models.
  • This framework offers a practical solution for controlling systems where dynamics are unknown or difficult to model.
  • The method demonstrates applicability to real-world complex systems like power grids and neural networks.