Instantaneous control of interacting particle systems in the mean-field limit
This work addresses the practical problem of controlling large-scale collective dynamics (e.g., evacuation) with minimal external agents, but the results are primarily numerical and incremental in nature.
The paper introduces an instantaneous control approach for steering interacting particle systems into a target region using a few external agents, demonstrating convergence of optimal controls and states in the mean-field limit as particle number increases.
Controlling large particle systems in collective dynamics by a few agents is a subject of high practical importance, e.g., in evacuation dynamics. In this paper we study an instantaneous control approach to steer an interacting particle system into a certain spatial region by repulsive forces from a few external agents, which might be interpreted as shepherd dogs leading sheep to their home. We introduce an appropriate mathematical model and the corresponding optimization problem. In particular, we are interested in the interaction of numerous particles, which can be approximated by a mean-field equation. Due to the high-dimensional phase space this will require a tailored optimization strategy. The arising control problems are solved using adjoint information to compute the descent directions. Numerical results on the microscopic and the macroscopic level indicate the convergence of optimal controls and optimal states in the mean-field limit,i.e., for an increasing number of particles.