Article
Robust coalitional model predictive control with predicted topology transitions
Author/s | Masero Rubio, Eva
Maestre Torreblanca, José María Ferramosca, Antonio Francisco, Mario Camacho, Eduardo F. |
Department | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Publication Date | 2021 |
Deposit Date | 2021-10-27 |
Published in |
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Abstract | This paper presents a novel clustering model predictive control technique where transitions to the best cooperation topology are planned over the prediction horizon. A new variable, the so-called transition horizon, is ... This paper presents a novel clustering model predictive control technique where transitions to the best cooperation topology are planned over the prediction horizon. A new variable, the so-called transition horizon, is added to the optimization problem to calculate the optimal instant to introduce the next topology. Accordingly, agents can predict topology transitions to adapt their trajectories while optimizing their goals. Moreover, conditions to guarantee recursive feasibility and robust stability of the system are provided. Finally, the proposed control method is tested via a simulated eight-coupled tanks plant. |
Funding agencies | European Union (UE). H2020 |
Project ID. | FPU18{04476
DPI2017-86918-R DPI2015-67341-C02-01 No. 789051 |
Citation | Masero Rubio, E., Maestre Torreblanca, J.M., Ferramosca, A., Francisco, M. y Camacho, E.F. (2021). Robust coalitional model predictive control with predicted topology transitions. IEEE Transactions on Control of Network Systems |
Files | Size | Format | View | Description |
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AcceptedManuscript_idus.pdf | 751.2Kb | [PDF] | View/ | |
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