Distributed Model-Free Adaptive Predictive Control for Urban Traffic Networks

Distributed Model-Free Adaptive Predictive Control for Urban Traffic Networks

Li, D. (Beijing Jiaotong Daxue)
De Schutter, B.H.K. (TU Delft Delft Center for Systems and Control; TU Delft Team Bart De Schutter)

Delft Center for Systems and Control

2022

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.

Adaptation models
Computational modeling
Data models
Data-driven control
distributed model predictive control (DMPC)
macroscopic fundamental diagram (MFD)
Mathematical model
model-free adaptive predictive control (MFAPC)
Predictive control
Predictive models
urban traffic network control.
Vehicle dynamics

http://resolver.tudelft.nl/uuid:cc180e06-28b4-4a95-842b-b305f2541391

https://doi.org/10.1109/TCST.2021.3059460

1063-6536

IEEE Transactions on Control Systems Technology, 30 (1), 180-192

Accepted Author Manuscript

Institutional Repository

journal article

© 2022 D. Li, B.H.K. De Schutter