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.
| Original language | English |
|---|---|
| Pages (from-to) | 180-192 |
| Journal | IEEE Transactions on Control Systems Technology |
| Volume | 30 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2022 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- 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