Moment-Driven Predictive Control of Mean-Field Collective Dynamics

The synthesis of control laws for interacting agent-based dynamics and their mean-field limit is studied. A linearization-based approach used the computation suboptimal feedback obtained from solution differential matrix Riccati equations. Quantification dynamic performance such leads to theoretical estimates on suitable linearization points nonlinear dynamics. Subsequently, are embedded into a model predictive framework where updated adaptively in time according information moments linear robustness proposed methodology assessed through different numerical experiments collective