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Data/Moment-Driven Approaches for Fast Predictive Control of Collective Dynamics

Author

Listed:
  • Giacomo Albi

    (Università di Verona)

  • Sara Bicego

    (Imperial College London, South Kensington Campus—SW72AZ)

  • Michael Herty

    (IGPM, RWTH Aachen University)

  • Yuyang Huang

    (Imperial College London, South Kensington Campus—SW72AZ)

  • Dante Kalise

    (Imperial College London, South Kensington Campus—SW72AZ)

  • Chiara Segala

    (Universit`a della Svizzera italiana—USI)

Abstract

Feedback control synthesis for large-scale particle systems is reviewed in the framework of model predictive control (MPC). The high-dimensional character of collective dynamics hampers the performance of traditional MPC algorithms based on fast online dynamic optimization at every time-step. Two alternatives to MPC are proposed. First, the use of supervised learning techniques for the offline approximation of optimal feedback laws is discussed. Then, a procedure based on a sequential linearization of the dynamics based on macroscopic quantities of the particle ensemble is reviewed. Both approaches circumvent the online solution of optimal control problems enabling fast, real-time, feedback synthesis for large-scale particle systems. Numerical experiments assess the performance of the proposed algorithms.

Suggested Citation

  • Giacomo Albi & Sara Bicego & Michael Herty & Yuyang Huang & Dante Kalise & Chiara Segala, 2025. "Data/Moment-Driven Approaches for Fast Predictive Control of Collective Dynamics," Dynamic Modeling and Econometrics in Economics and Finance,, Springer.
    • Handle: RePEc:spr:dymchp:978-3-031-85256-5_2
    DOI: 10.1007/978-3-031-85256-5_2
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