Scenario-based Distributed Model Predictive Control for freeway networks

Liu, S.; Sadowska, A.D.; Hellendoorn, J.; De Schutter, Bart

Scenario-based Distributed Model Predictive Control for freeway networks

Conference paper (2016)

Authors

S. Liu

A.D. Sadowska , Schlumberger Gould Research Center

J. Hellendoorn Delft Center for Systems and Control

Bart De Schutter

Department

Delft Center for Systems and Control

Linear programming Robustness Uncertainty Optimal control Traffic control Predictive control Couplings

To reference this document use:

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

More Info

expand_more

Published Date

2016

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Department

Delft Center for Systems and Control

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.