Robust coalitional model predictive control with predicted topology transitions
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 t...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/126921 |
| Acceso en línea: | https://hdl.handle.net/11441/126921 https://doi.org/10.1109/TCNS.2021.3088806 |
| Access Level: | acceso abierto |
| Palabra clave: | Model predictive control Control by clustering Distributed control Coalitional control Networked control |
| Sumario: | 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. |
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