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Hierarchical MPC for Multi-Commodity Transportation Networks

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dc.contributor.authorNabais, João Lemos
dc.contributor.authorNegenborn, Rudy R.
dc.contributor.authorCarmona-Benítez, Rafael Bernardo
dc.contributor.authorMendonça, Luís Filipe
dc.contributor.authorBotto, Miguel Ayala
dc.date.accessioned2024-12-16T17:11:33Z
dc.date.available2024-12-16T17:11:33Z
dc.date.issued2014
dc.description.abstractTransportation networks are large scale complex systems spatially distributed whose objective is to deliver commodities at the agreed time and at the agreed location. These networks appear in different domain fields, such as communication, water distribution, traffic, logistics and transportation. A transportation network has at the macroscopic level storage capability (located in the nodes) and transport delay (along each connection) as main features. Operations management at transportation networks can be seen as a flow assignment problem. The problem dimension to solve grows exponentially with the number of existing commodities, nodes and connections. In this work we present a Hierarchical Model Predictive Control (H-MPC) architecture to determine flow assignments in transportation networks, while minimizing exogenous inputs effects. This approach has the capacity to keep track of commodity types while solving the flow assignment problem. A flow decomposition of the main system into subsystems is proposed to diminish the problem dimension to solve in each time step. Each subsystem is managed by a control agent. Control agents solve their problems in a hierarchical way, using a so-called push-pull flow perspective. Further problem dimension reduction is achieved using contracted projection sets. The framework proposed can be easily scaled to network topologies in which hundreds of commodities and connections are present.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationNabais, J., Negenborn, R. R., Carmona Benítez, R. B., Mendonça, L. F. & Ayala Botto, M. (2014). Hierarchical Model Predictive Control for Multi-Commodity Transportation Networks. In José M. Maestre & Rudy R. Negenborn (eds), Distributed MPC Made Easy (pp. 535-552). Netherlands: Springer.pt_PT
dc.identifier.doihttps://doi.org/10.1007/978-94-007-7006-5_33pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.26/53190
dc.language.isoengpt_PT
dc.publisherSpringerpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.titleHierarchical MPC for Multi-Commodity Transportation Networkspt_PT
dc.typebook part
dspace.entity.typePublication
oaire.citation.endPage552pt_PT
oaire.citation.startPage535pt_PT
oaire.citation.titleDistributed Model Predictive Control Made Easy. Intelligent Systems, Control and Automation: Science and Engineeringpt_PT
oaire.citation.volume69pt_PT
rcaap.rightsclosedAccesspt_PT
rcaap.typebookPartpt_PT

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