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Title
Japanese: 
English:Fast Clustering for Multi-agent Model Predictive Control 
Author
Japanese: P. Chanfreut, J.M. Maestre, 畑中 健志, E.F. Camacho.  
English: P. Chanfreut, J.M. Maestre, T. Hatanaka, E.F. Camacho.  
Language English 
Journal/Book name
Japanese: 
English:IEEE Transactions on Control of Network Systems 
Volume, Number, Page vol. 9    no. 3    pp. 1544-1555
Published date Mar. 2022 
Publisher
Japanese: 
English:IEEE 
Conference name
Japanese: 
English: 
Conference site
Japanese: 
English: 
DOI https://doi.org/10.1109/TCNS.2022.3158745
Abstract 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 (BQP), where each variable represents one agent-to-agent connection. A supervisory layer decides periodically the number and composition of the coalitions by solving the BQP while, at a bottom layer, each cluster computes the control inputs. The performance of this method is illustrated through numerical examples.

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