Data-driven robust predictive control for mixed vehicle platoons using noisy measurement

Lan, J. , Zhao, D. and Tian, D. (2023) Data-driven robust predictive control for mixed vehicle platoons using noisy measurement. IEEE Transactions on Intelligent Transportation Systems, 24(6), pp. 6586-6596. (doi: 10.1109/TITS.2021.3128406)

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This paper investigates cooperative adaptive cruise control (CACC) for mixed platoons consisting of both human-driven vehicles (HVs) and automated vehicles (AVs). This research is critical because the penetration rate of AVs in the transportation system will remain unsaturated for a long time. Uncertainties and randomness are prevalent in human driving behaviours and highly affect the platoon safety and stability, which need to be considered in the CACC design. A further challenge is the difficulty to know the exact models of the HVs and the exact powertrain parameters of both AVs and HVs. To address these challenges, this paper proposes a data-driven model predictive control (MPC) that does not need the exact models of HVs or powertrain parameters. The MPC design adopts the technique of data-driven reachability to predict the future trajectory of the mixed platoon within a given horizon based on noisy vehicle measurements. Compared to the classic adaptive cruise control (ACC) and existing data-driven adaptive dynamic programming (ADP), the proposed MPC ensures satisfaction of constraints such as acceleration limit and safe inter-vehicular gap. With this salient feature, the proposed MPC has provably guarantee in establishing a safe and robustly stable mixed platoon despite of the velocity changes of the leading vehicle. The efficacy and advantage of the proposed MPC are verified through comparison with the classic ACC and data-driven ADP methods on both small and large mixed platoons.

Item Type:Articles
Additional Information:This work was supported in part by the Engineering and Physical Sciences Research Council of the UK under the EPSRC Innovation Fellowship (EP/S001956/1), in part by the UK Royal Society-Newton Advanced Fellowship (NAF\R1\201213), and in part by the National Natural Science Foundation of China (62061130221).
Glasgow Author(s) Enlighten ID:Zhao, Dr Dezong and Lan, Dr Jianglin
Authors: Lan, J., Zhao, D., and Tian, D.
College/School:College of Science and Engineering > School of Engineering
Journal Name:IEEE Transactions on Intelligent Transportation Systems
ISSN (Online):1558-0016
Published Online:24 November 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in IEEE Transactions on Intelligent Transportation Systems 24(6): 6586-6596
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
314774Toward Energy Efficient Autonomous Vehiciles via Cloud-Aided learningDezong ZhaoEngineering and Physical Sciences Research Council (EPSRC)EP/S001956/2ENG - Autonomous Systems & Connectivity