Robust min-max model predictive vehicle platooning with causal disturbance feedback

Zhou, J., Tian, D., Sheng, Z., Duan, X., Qu, G., Zhao, D. , Cao, D. and Shen, X. (2022) Robust min-max model predictive vehicle platooning with causal disturbance feedback. IEEE Transactions on Intelligent Transportation Systems, 23(9), pp. 15878-15897. (doi: 10.1109/TITS.2022.3146149)

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Abstract

Platoon-based vehicular cyber-physical systems have gained increasing attention due to their potentials in improving traffic efficiency, capacity, and saving energy. However, external uncertain disturbances arising from mismatched model errors, sensor noises, communication delays and unknown environments can impose a great challenge on the constrained control of vehicle platooning. In this paper, we propose a closed-loop min-max model predictive control (MPC) with causal disturbance feedback for vehicle platooning. Specifically, we first develop a compact form of a centralized vehicle platooning model subject to external disturbances, which also incorporates the lower-level vehicle dynamics. We then formulate the uncertain optimal control of the vehicle platoon as a worst-case constrained optimization problem and derive its robust counterpart by semidefinite relaxation. Thus, we design a causal disturbance feedback structure with the robust counterpart, which leads to a closed-loop min-max MPC platoon control solution. Even though the min-max MPC follows a centralized paradigm, its robust counterpart can keep the convexity and enable the efficient and practical implementation of current convex optimization techniques. We also derive a linear matrix inequality (LMI) condition for guaranteeing the recursive feasibility and input-to-state practical stability (ISpS) of the platoon system. Finally, simulation results are provided to verify the effectiveness and advantage of the proposed MPC in terms of constraint satisfaction, platoon stability and robustness against different external disturbances.

Item Type:Articles
Additional Information:This work was supported in part by the National Postdoctoral Program for Innovative Talents under Grant BX2021027, in part by the China Postdoctoral Science Foundation under Grant 2020M680299, in part by the Opening Project of Ministry of Transport Key Laboratory of Technology on Intelligent Transportation Systems under Grant F20211746, in part by the National Natural Science Foundation of China under Grant U20A20155 and Grant 61822101, in part by the Beijing Municipal Natural Science Foundation under Grant L191001, and in part by the Newton Advanced Fellowship under Grant 62061130221.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Zhao, Dr Dezong
Authors: Zhou, J., Tian, D., Sheng, Z., Duan, X., Qu, G., Zhao, D., Cao, D., and Shen, X.
College/School:College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
Journal Name:IEEE Transactions on Intelligent Transportation Systems
Publisher:IEEE
ISSN:1524-9050
ISSN (Online):1558-0016
Published Online:03 February 2022
Copyright Holders:Copyright © 2022 IEEE
First Published:First published in IEEE Transactions on Intelligent Transportation Systems 23(9): 15878-15897
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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