TY - JOUR
T1 - Distributed Cloud Model Predictive Control With Delay Compensation for Heterogeneous Vehicle Platoons
AU - Zhao, Junxiao
AU - Ma, Yaling
AU - Dai, Li
AU - Sun, Zhongqi
AU - Chen, Hanli
AU - Xia, Yuanqing
N1 - Publisher Copyright:
© 1967-2012 IEEE.
PY - 2025
Y1 - 2025
N2 - This paper proposes a distributed cloud model predictive control (DCMPC) approach with delay compensation tailored for heterogeneous vehicle platoons. Leveraging the cloud's extensive computational power and scalability, this method aims to enhance control over complex vehicle platoon systems. Despite the benefits, network delays inherent in cloud-based systems can severely impact control performance. To address this challenge, our method incorporates a tube-based model predictive control (MPC) framework. This innovative approach effectively compensates for the delays associated with cloud control processes. In our solution, the DCMPC algorithm, which is computationally demanding, is executed in the cloud. Simultaneously, a feedback compensator is implemented at the vehicle edge to improve the robustness of the system and ensure coordinated platoon control. We validate the proposed algorithm through simulation experiments involving heterogeneous vehicle platoons. The results demonstrate the algorithm's effectiveness across various scenarios with different delay upper bounds, confirming its capability to manage delays and maintain good control performance.
AB - This paper proposes a distributed cloud model predictive control (DCMPC) approach with delay compensation tailored for heterogeneous vehicle platoons. Leveraging the cloud's extensive computational power and scalability, this method aims to enhance control over complex vehicle platoon systems. Despite the benefits, network delays inherent in cloud-based systems can severely impact control performance. To address this challenge, our method incorporates a tube-based model predictive control (MPC) framework. This innovative approach effectively compensates for the delays associated with cloud control processes. In our solution, the DCMPC algorithm, which is computationally demanding, is executed in the cloud. Simultaneously, a feedback compensator is implemented at the vehicle edge to improve the robustness of the system and ensure coordinated platoon control. We validate the proposed algorithm through simulation experiments involving heterogeneous vehicle platoons. The results demonstrate the algorithm's effectiveness across various scenarios with different delay upper bounds, confirming its capability to manage delays and maintain good control performance.
KW - Cloud computing
KW - delay compensation
KW - distributed model predictive control (DMPC)
KW - tube-based model predictive control (MPC)
KW - vehicle platooning
UR - https://www.scopus.com/pages/publications/105001794663
U2 - 10.1109/TVT.2025.3555172
DO - 10.1109/TVT.2025.3555172
M3 - Article
AN - SCOPUS:105001794663
SN - 0018-9545
VL - 74
SP - 11793
EP - 11805
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 8
ER -