Scenario-based distributed model predictive control for freeway networks


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 entry:

@inproceedings{LiuSad:16-020,
author={S. Liu and A. Sadowska and H. Hellendoorn and B. {D}e Schutter},
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},
doi={10.1109/ITSC.2016.7795799}
}


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