This paper provides a systematic review of emerging control techniques used for railway Virtual Coupling (VC) studies. Train motion models are first reviewed, including model formulations and the force elements involved. Control objectives and typical design constraints are then elaborated. Next, the existing VC control techniques are surveyed and classified into five groups: consensus-based control, model prediction control, sliding mode control, machine learning-based control, and constraints-following control. Their advantages and disadvantages for VC applications are also discussed in detail. Furthermore, several future studies for achieving better controller development and implementation, respectively, are presented. The purposes of this survey are to help researchers to achieve a better systematic understanding regarding VC control, to spark more research into VC and to further speed-up the realization of this emerging technology in railway and other relevant fields such as road vehicles.

翻译：本文系统综述了铁路虚拟编组（Virtual Coupling，VC）研究中采用的新兴控制技术。首先回顾了列车运动模型，包括模型公式及涉及的力元素；继而详细阐述了控制目标与典型设计约束。接着，对现有VC控制技术进行梳理，并将其分为五类：基于一致性的控制、模型预测控制、滑模控制、基于机器学习的控制以及约束跟随控制，同时深入讨论了各类技术应用于VC的优势与局限性。此外，本文提出了若干未来研究方向，以分别促进控制器开发与实现的改进。本综述旨在帮助研究者系统理解VC控制、激发更多VC相关研究，并进一步加速该新兴技术在铁路及公路车辆等相关领域的实际应用。