Fast Clustering for Multi-agent Model Predictive Control

Paula Chanfreut

doi:10.1109/TCNS.2022.3158745

Fast Clustering for Multi-agent Model Predictive Control

2022-09-01

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AI

DOI：10.1109/TCNS.2022.3158745

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In coalitional model predictive control, the overall system is controlled by a set of networked agents that are dynamically arranged into clusters of connected agents that coordinate their actions, also called coalitions. In this way, the overall coordination burden and the need for sharing information are reduced. In this article, the clustering problem is formulated as a binary quadratic program (BOP), where each variable represents one agentto-agent connection. A supervisory layer decides periodically the number and composition of the coalitions by solving the BOP while, at a bottom layer, each cluster computes the control inputs. The performance of this method is illustrated through numerical examples.

Keywords:

Coalitional control

control by clustering

distributed model predictive control

network topologies

Fast Clustering for Multi-agent Model Predictive Control

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