Distributed Model-Free Adaptive Predictive Control for Urban Traffic Networks

Journal article (2022)

Authors

D. Li Beijing Jiaotong University
B.H.K. De Schutter Team Bart De Schutter - Mechanical, Maritime and Materials Engineering , Delft Center for Systems and Control
Research Group
Team Bart De Schutter (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 D. Li, B.H.K. De Schutter
DOI: https://doi.org/10.1109/TCST.2021.3059460
Mathematical model Vehicle dynamics Predictive models Computational modeling Adaptation models Data models Predictive control Data-driven control Distributed model predictive control (DMPC) Macroscopic fundamental diagram (MFD) Model-free adaptive predictive control (MFAPC) Urban traffic network control.
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Published Date

2022

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Bart De Schutter

Abstract

Data-driven control without using mathematical models is a promising research direction for urban traffic control due to the massive amounts of traffic data generated every day. This article proposes a novel distributed model-free adaptive predictive control (D-MFAPC) approach for multiregion urban traffic networks. More specifically, the traffic dynamics of the network regions are first transformed into MFAPC data models, and then, the derived MFAPC data models instead of mathematical traffic models serve as the prediction models in the distributed control design. The formulated control problem is finally solved with an alternating direction method of multipliers (ADMM)-based approach. The simulation results for the traffic network of Linfen, Shanxi, China, show the feasibility and effectiveness of the proposed method.