Freeway Traffic Density and On-Ramp Queue Control via ILC Approach

A new queue length information fused iterative learning control approach (QLIF-ILC) is presented for freeway traffic ramp metering to achieve a better performance by utilizing the error information of the on-ramp queue length. The QLIF-ILC consists of two parts, where the iterative feedforward part...

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Bibliographic Details
Published in:Mathematical problems in engineering 2014-01, Vol.2014 (1)
Main Authors: Chi, Ronghu, Li, Mengze, Hou, Zhongsheng, Liu, Xiangpeng, Yu, Zhaoxu
Format: Article
Language:English
Subjects:
Colleges & universities
Control data (computers)
Control systems
Control theory
Error analysis
Feedforward control
Freeways
Highways
Iterative methods
Learning
Markov analysis
Mathematical problems
Methods
Neural networks
Queues
Queuing theory
Roads & highways
Robust control
Science
Tracking control
Tracking errors
Traffic congestion
Traffic control
Traffic engineering
Traffic flow
Traffic information
Traffic volume
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Summary:A new queue length information fused iterative learning control approach (QLIF-ILC) is presented for freeway traffic ramp metering to achieve a better performance by utilizing the error information of the on-ramp queue length. The QLIF-ILC consists of two parts, where the iterative feedforward part updates the control input signal by learning from the past control data in previous trials, and the current feedback part utilizes the tracking error of the current learning iteration to stabilize the controlled plant. These two parts are combined in a complementary manner to enhance the robustness of the proposed QLIF-ILC. A systematic approach is developed to analyze the convergence and robustness of the proposed learning scheme. The simulation results are further given to demonstrate the effectiveness of the proposed QLIF-ILC.
ISSN:1024-123X
1563-5147
DOI:10.1155/2014/321934