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Control of complex systems with generalized embedding and empirical dynamic modeling.

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Summary
This summary is machine-generated.

This study introduces a novel data-driven approach for controlling complex nonlinear systems using generalized state space embedding and model predictive control. This method offers explainable control without needing complex mathematical models or extensive training.

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Area of Science:

  • Control Engineering
  • Complex Systems Science
  • Data-Driven Modeling

Background:

  • Effective system control relies on understanding process dynamics, often achieved through mathematical or data-driven models.
  • Complex systems pose challenges for traditional modeling, leading to abstract data-driven approaches lacking clear connections to dynamics or requiring extensive training.
  • Existing methods struggle with the complexity and explainability required for advanced control applications.

Purpose of the Study:

  • To present a novel method for model predictive control (MPC) using generalized state space embedding.
  • To provide a data-driven and explainable approach for controlling nonlinear, complex systems.
  • To demonstrate the applicability of this method across various control and dynamic systems.

Main Methods:

  • Developed a generalized state space embedding technique to represent complex system dynamics.
  • Integrated this embedding with model predictive control (MPC) for system guidance.
  • Validated the approach on nonlinear dynamics generated by a large-scale agent-based model (1200 agents).

Main Results:

  • Successfully demonstrated model predictive control from generalized state space embedding.
  • The method provides a data-driven and explainable control strategy for complex nonlinear systems.
  • The approach was effective even for dynamics generated by a complex agent-based model.

Conclusions:

  • Generalized state space embedding offers a powerful tool for data-driven control of complex systems.
  • This method overcomes limitations of traditional mathematical modeling and abstract data-driven techniques.
  • The approach is broadly applicable to any controller and dynamic system representable in a state space.