Reference
S. Liu, A. Sadowska, H. Hellendoorn, and B. De Schutter, "Scenario-based
distributed model predictive control for freeway networks,"
Proceedings of the 2016 IEEE 19th International Conference on
Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil, pp.
1779-1784, Nov. 2016.
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.
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BibTeX
@inproceedings{LiuSad:16-020,
author = {Liu, Shuai and Sadowska, Anna and Hellendoorn, Hans and De
Schutter, Bart},
title = {Scenario-Based Distributed Model Predictive Control for Freeway
Networks},
booktitle = {Proceedings of the 2016 IEEE 19th International Conference on
Intelligent Transportation Systems (ITSC)},
address = {Rio de Janeiro, Brazil},
pages = {1779--1784},
month = nov,
year = {2016}
}