TY - JOUR
T1 - Asynchronous self-triggered stochastic distributed MPC for cooperative vehicle platooning over vehicular ad-hoc networks
AU - Chen, Jicheng
AU - Wei, Henglai
AU - Zhang, Hui
AU - Shi, Yang
N1 - Publisher Copyright:
© 1967-2012 IEEE.
PY - 2023/11/1
Y1 - 2023/11/1
N2 - In this article, 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.
AB - In this article, 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.
KW - Distributed control
KW - Model predictive control (MPC)
KW - Platoon systems
KW - Self-Triggered control
UR - https://www.scopus.com/pages/publications/85163779147
U2 - 10.1109/TVT.2023.3288210
DO - 10.1109/TVT.2023.3288210
M3 - 文章
AN - SCOPUS:85163779147
SN - 0018-9545
VL - 72
SP - 14061
EP - 14073
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 11
ER -