Artículo
Fast clustering for multi-agent model predictive control
Autor/es | Chanfreut Palacio, Paula
Maestre Torreblanca, José María Hatanaka, Takeshi Camacho, Eduardo F. |
Departamento | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2022-09 |
Fecha de depósito | 2023-03-01 |
Publicado en |
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Resumen | 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 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. |
Cita | Chanfreut, P., Maestre Torreblanca, J.M., Hatanaka, T. y Camacho, E.F. (2022). Fast clustering for multi-agent model predictive control. IEEE Transactions on Control of Network Systems, 9 (3), 1544-1555. https://doi.org/10.1109/TCNS.2022.3158745. |
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