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

Giacomo Albi (), Sara Bicego (), Michael Herty (), Yuyang Huang (), Dante Kalise () and Chiara Segala ()
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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

Chapter Chapter 2 in Model Predictive Control, 2025, pp 29-54 from Springer

Abstract: 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.

Date: 2025
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DOI: 10.1007/978-3-031-85256-5_2

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