A Data-Driven Iterative Feedback Tuning Approach of ALINEA for Freeway Traffic Ramp Metering With PARAMICS Simulations

In this work, a new iterative feedback tuning approach is proposed to tune ALINEA's controller gain automatically when there is not enough prior information available to select a proper feedback gain of ALINEA. It is a data-driven method and the ALINEA controller is auto-tuned only depending on...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2013-11, Vol.9 (4), p.2310-2317
Main Authors: Chi, Ronghu, Hou, Zhongsheng, Jin, Shangtai, Wang, Danwei, Hao, Jiangen
Format: Article
Language:English
Subjects:
Adaptive control
ALINEA control
Data models
data-driven approach
FL-ALINEA
iterative feedback tuning
Iterative methods
PARAMICS simulator
Traffic control
Tuning
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Summary:In this work, a new iterative feedback tuning approach is proposed to tune ALINEA's controller gain automatically when there is not enough prior information available to select a proper feedback gain of ALINEA. It is a data-driven method and the ALINEA controller is auto-tuned only depending on the input and output data collected from closed-loop experiments. To mimic a real traffic environment, a simulator is built on the PARAMICS platform. The flow-based ALINEA controller is also considered to illustrate the good tuning performance of IFT comprehensively. The effectiveness of the proposed methods is verified through PARAMICS based simulations.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2013.2238548