585 RWTH Publication No: 811898        2021       
TITLE Moment-Driven Predictive Control of Mean-Field Collective Dynamics
AUTHORS G. Albi, Michael Herty, D. Kalise, Chiara Segala
ABSTRACT The synthesis of control laws for interacting agent-based dynamics and their mean-field limit is studied. A linearization-based approach is used for the computation of sub-optimal feedback laws obtained from the solution of differential matrix Riccati equations. Quantification of dynamic performance of such control laws leads to theoretical estimates on suitable linearization points of the nonlinear dynamics. Subsequently, the feedback laws are embedded into nonlinear model predictive control framework where the control is updated adaptively in time according to dynamic information on moments of linear mean-field dynamics. The performance and robustness of the proposed methodology is assessed through different numerical experiments in collective dynamics.
KEYWORDS Agent-based dynamics, mean-field equations, optimal feedback control, Riccati equations, nonlinear model predictive control
DOI 10.1137/21M1391559
PUBLICATION SIAM Journal on Control and Optimization, Vol. 60, Iss. 2 (2022)