Scenario-based Distributed Model Predictive Control for freeway networks

Shuai Liu, Anna Sadowska, Hans Hellendoorn, Bart De Schutter

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)

Abstract

In this paper we develop a scenario-based Distributed Model Predictive Control (DMPC) approach for large-scale freeway networks. The uncertainties in a large-scale freeway network are categorized into global uncertainties for the overall network and local uncertainties for subnetworks. A reduced scenario tree is proposed, consisting of global scenarios and a reduced local scenario tree. For handling uncertainties in the scenario-based DMPC problem, a min-max setting is considered. A case study is implemented for investigating the scenario-based DMPC approach, and the results show that in the presence of uncertainties it is effective in improving the control performance with the queue length constraint being satisfied.

Original languageEnglish
Title of host publicationProceedings of the IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016
EditorsR. Rosetti, D. Wolf
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Pages1779-1784
ISBN (Electronic)978-1-5090-1889-5
DOIs
Publication statusPublished - 2016
EventITSC 2016: 19th International Conference on Intelligent Transportation Systems - Rio de Janeiro, Brazil
Duration: 1 Nov 20164 Dec 2016
Conference number: 19

Conference

ConferenceITSC 2016: 19th International Conference on Intelligent Transportation Systems
Abbreviated titleITSC 2016
CountryBrazil
CityRio de Janeiro
Period1/11/164/12/16

Keywords

  • Uncertainty
  • Traffic control
  • Robustness
  • Linear programming
  • Predictive control
  • Optimal control
  • Couplings

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