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Title: Asynchronous self-triggered stochastic distributed MPC for cooperative vehicle platooning over vehicular ad-hoc networks
Authors: Chen, Jicheng
Wei, Henglai
Zhang, Hui
Shi, Yang
Keywords: Engineering::Mechanical engineering
Issue Date: 2023
Source: Chen, J., Wei, H., Zhang, H. & Shi, Y. (2023). Asynchronous self-triggered stochastic distributed MPC for cooperative vehicle platooning over vehicular ad-hoc networks. IEEE Transactions On Vehicular Technology.
Journal: IEEE Transactions on Vehicular Technology
Abstract: In this paper, an asynchronous stochastic self-triggered distributed MPC (DMPC) control scheme is proposed for vehicular platoon systems under coupled state constraints and additive stochastic disturbance. In considered platoon systems, each vehicle broadcasts its predicted state as beacon information to its neighbouring vehicles through the vehicular ad-hoc network (VANET). To reduce the communication burden in the VANET, each vehicle proactively determines the next sampling time instant by solving the stochastic self-triggered DMPC problem at the sampling time instant. The self-triggered problem is formulated by utilizing local vehicle states and asynchronous beacon information from its neighbours. Consequently, the proposed scheme reduces the communication load dramatically in the VANET while maintaining a satisfactory control performance compared to periodic time-triggered stochastic DMPC. To handle the state coupling between vehicles, e.g., for collision avoidance or communication connectivity purpose, probabilistic coupled state constraints are incorporated into the DMPC problem. Based on the information on stochastic disturbance, the probabilistic coupled state constraints are transformed into deterministic forms using the stochastic tube-based method. Theoretical analysis has shown that closed-loop platooning is quadratically stable at triggering time instants. Numerical examples illustrate the efficacy of the proposed control method in terms of data transmission reduction.
ISSN: 0018-9545
DOI: 10.1109/TVT.2023.3288210
Schools: School of Mechanical and Aerospace Engineering 
Rights: © 2023 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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