Original scientific paper
https://doi.org/10.7307/ptt.v38i6.1357
Bandwidth-Aware Event-Triggered Adaptive DMPC for Vehicle Platoon Control
Yiming Song
orcid.org/0009-0002-3748-825X
; School of Automation, Guangxi University of Science and Technology, Liuzhou, China
Hongtao Ye
; School of Automation, Guangxi University of Science and Technology, Liuzhou, China; Guangxi Key Laboratory of Automobile Components and Vehicle Technology, Guangxi University of Science and Technology, Liuzhou, China
*
Wenguang Luo
; School of Automation, Guangxi University of Science and Technology, Liuzhou, China
Xiaohua Zhou
; School of Automation, Guangxi University of Science and Technology, Liuzhou, China
Jiayan Wen
; Guangxi Key Laboratory of Automobile Components and Vehicle Technology, Guangxi University of Science and Technology, Liuzhou, China
* Corresponding author.
Abstract
Self-driving vehicle platoons offer promising improvements in traffic efficiency and safety. However, their deployment is hindered by limited communication bandwidth and computational constraints in distributed control systems. This paper presents a bandwidth‑aware event‑triggered adaptive distributed model predictive control (DMPC) method. Firstly, to mitigate resource waste from frequent communications under bandwidth-constrained conditions in conventional DMPC, a bandwidth-aware event-triggered mechanism is designed based on a sigmoid threshold function of vehicle state errors. This mechanism adjusts the triggering threshold according to bandwidth availability, thereby suppressing unnecessary transmissions while maintaining a balance between communication efficiency and control performance. Secondly, a dual-input fuzzy adaptive module is introduced to reduce the computational burden in vehicle platoon control. This module takes maximum position and velocity errors as inputs to tune the prediction horizon dynamically. Finally, numerical simulation results show that under high bandwidth usage, the proposed method reduces communication resource consumption to 57.3%, while decreasing the communication frequency by up to 15.7% compared with the existing method. Meanwhile, it decreases the controller’s computational overhead by up to 20.45% relative to fixed-horizon DMPC. The proposed approach enhances both communication and computational efficiency, making it applicable for resource‑constrained platoon control scenarios.
Keywords
vehicle platoon; distributed model predictive control; event-triggered mechanism; bandwidth-aware; adaptive prediction horizon; fuzzy control
Hrčak ID:
348611
URI
Publication date:
29.6.2026.
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