Scenario-based Distributed Model Predictive Control for freeway networks

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Scenario-based Distributed Model Predictive Control for freeway networks

Conference Paper (2016)

Author(s)

Shuai Liu (TU Delft - Team Bart De Schutter)

Anna Sadowska (TU Delft - Team Bart De Schutter, Schlumberger Gould Research Center)

Hans Hellendoorn (TU Delft - Delft Center for Systems and Control)

Bart De Schutter (TU Delft - Team Bart De Schutter)

Department

Delft Center for Systems and Control

Linear programming Robustness Uncertainty Optimal control Traffic control Predictive control Couplings

DOI related publication

https://doi.org/10.1109/ITSC.2016.7795799 Final published version

To reference this document use

https://resolver.tudelft.nl/uuid:6aee4234-4f43-4207-b1c4-550303c11059

More Info

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Publication Year

2016

Language

English

Department

Delft Center for Systems and Control

Pages (from-to)

1779-1784

ISBN (electronic)

978-1-5090-1889-5

Event

ITSC 2016: 19th International Conference on Intelligent Transportation Systems (2016-11-01 - 2016-12-04), Rio de Janeiro, Brazil

Downloads counter

211

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.