Traffic signal hybrid control method based on iterative learning and model predictive control

被引：0

作者：

Yan F. ^{[1
]}

Li P. ^{[1
]}

Xu X.-Y. ^{[1
]}

机构：

[1] College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan

来源：

Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2021年 / 38卷 / 03期

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

Convergence analysis; Iterative learning control; Model predictive control; Traffic signal control;

D O I：

10.7641/CTA.2020.91025

中图分类号：

学科分类号：

摘要：

The traffic signal control method based on iterative learning control can not effectively deal with the nonrepetitive real-time disturbance in the road network. Based on the iterative learning traffic signal control method, a mixed traffic signal control method based on iterative learning and model predictive control is proposed through combining the rolling optimization and real-time correction characteristics of model predictive control. The proposed method can effectively improve the traffic conditions of the road network by using the periodic characteristics of traffic flow and deal with the real-time disturbance through the advantages of model predictive control. Thus, the control efficiency of the traffic signals is further improved. The effectiveness of the proposed method is verified by simulation experiments. The experimental results show that the hybrid traffic signal control method based on iterative learning and model predictive control can more effectively balance the vehicle density in the road network, and further improve the traffic efficiency of the road network. Finally, the convergence of the proposed method is also analyzed. © 2021, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.

引用

页码：339 / 348

页数：9

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