Scenario-Based Distributed Model Predictive Control for Freeway Networks

被引：0
|
作者：
Liu, Shuai [1 ]
Sadowska, Anna [2 ]
Hellendoorn, Hans [1 ]
De Schutter, Bart [1 ]
机构：
[1] Delft Univ Technol, Delft Ctr Syst & Control, Delft, Netherlands
[2] Schlumberger Gould Res Ctr, Cambridge, England
来源：
2016 IEEE 19TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) | 2016年
关键词：
ROBUST-CONTROL;
D O I：
暂无
中图分类号：
U [交通运输];
学科分类号：
08 ; 0823 ;
摘要：
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.
引用
收藏
页码：1779 / 1784
页数：6
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