Scenario-based Distributed Model Predictive Control for freeway networks

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

doi:10.1109/ITSC.2016.7795799

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 volume › Conference contribution › Scientific › peer-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 language	English
Title of host publication	Proceedings of the IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016
Editors	R. Rosetti, D. Wolf
Place of Publication	Piscataway, NJ, USA
Publisher	IEEE
Pages	1779-1784
ISBN (Electronic)	978-1-5090-1889-5
DOIs	https://doi.org/10.1109/ITSC.2016.7795799
Publication status	Published - 2016
Event	ITSC 2016: 19th International Conference on Intelligent Transportation Systems - Rio de Janeiro, Brazil Duration: 1 Nov 2016 → 4 Dec 2016 Conference number: 19

Conference

Conference	ITSC 2016: 19th International Conference on Intelligent Transportation Systems
Abbreviated title	ITSC 2016
Country/Territory	Brazil
City	Rio de Janeiro
Period	1/11/16 → 4/12/16

Keywords

Uncertainty
Traffic control
Robustness
Linear programming
Predictive control
Optimal control
Couplings

Access to Document

10.1109/ITSC.2016.7795799

Cite this

@inproceedings{6aee42344f434207b1c4550303c11059,

title = "Scenario-based Distributed Model Predictive Control for freeway networks",

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.",

keywords = "Uncertainty, Traffic control, Robustness, Linear programming, Predictive control, Optimal control, Couplings",

author = "Shuai Liu and Anna Sadowska and Hans Hellendoorn and {De Schutter}, Bart",

year = "2016",

doi = "10.1109/ITSC.2016.7795799",

language = "English",

pages = "1779--1784",

editor = "R. Rosetti and D. Wolf",

booktitle = "Proceedings of the IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016",

publisher = "IEEE",

address = "United States",

note = "ITSC 2016: 19th International Conference on Intelligent Transportation Systems, ITSC 2016 ; Conference date: 01-11-2016 Through 04-12-2016",

}

Liu, S, Sadowska, A, Hellendoorn, H & De Schutter, B 2016, Scenario-based Distributed Model Predictive Control for freeway networks. in R Rosetti & D Wolf (eds), Proceedings of the IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016. IEEE, Piscataway, NJ, USA, pp. 1779-1784, ITSC 2016: 19th International Conference on Intelligent Transportation Systems, Rio de Janeiro, Brazil, 1/11/16. https://doi.org/10.1109/ITSC.2016.7795799

Scenario-based Distributed Model Predictive Control for freeway networks. / Liu, Shuai; Sadowska, Anna; Hellendoorn, Hans et al.
Proceedings of the IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016. ed. / R. Rosetti; D. Wolf. Piscataway, NJ, USA: IEEE, 2016. p. 1779-1784.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Scenario-based Distributed Model Predictive Control for freeway networks

AU - Liu, Shuai

AU - Sadowska, Anna

AU - Hellendoorn, Hans

AU - De Schutter, Bart

N1 - Conference code: 19

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Uncertainty

KW - Traffic control

KW - Robustness

KW - Linear programming

KW - Predictive control

KW - Optimal control

KW - Couplings

U2 - 10.1109/ITSC.2016.7795799

DO - 10.1109/ITSC.2016.7795799

M3 - Conference contribution

SP - 1779

EP - 1784

BT - Proceedings of the IEEE 19th International Conference on Intelligent Transportation Systems, ITSC 2016

A2 - Rosetti, R.

A2 - Wolf, D.

PB - IEEE

CY - Piscataway, NJ, USA

T2 - ITSC 2016: 19th International Conference on Intelligent Transportation Systems

Y2 - 1 November 2016 through 4 December 2016

ER -

Scenario-based Distributed Model Predictive Control for freeway networks

Abstract

Conference

Keywords

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