Selective model-predictive control for flocking systems

TL;DR

This paper develops a selective model predictive control approach for large flocking systems, introducing a mean-field limit and stochastic algorithms to efficiently achieve consensus among agents.

Contribution

It presents a novel selective control framework with a mean-field approximation and stochastic algorithms for efficient large-scale flocking control.

Findings

The proposed MPC scheme effectively enhances consensus.

Mean-field limit simplifies large-agent system analysis.

Numerical simulations demonstrate the method's efficiency.

Abstract

In this paper the optimal control of alignment models composed by a large number of agents is investigated in presence of a selective action of a controller, acting in order to enhance consensus. Two types of selective controls have been presented: an homogeneous control filtered by a selective function and a distributed control active only on a selective set. As a first step toward a reduction of computational cost, we introduce a model predictive control (MPC) approximation by deriving a numerical scheme with a feedback selective constrained dynamics. Next, in order to cope with the numerical solution of a large number of interacting agents, we derive the mean-field limit of the feedback selective constrained dynamics, which eventually will be solved numerically by means of a stochastic algorithm, able to simulate efficiently the selective constrained dynamics. Finally, several numerical simulations are reported to show the efficiency of the proposed techniques.