Improving transportation efficiency is an eternal research hotspot in rail transit system. In recent years, the train operation control method based on virtual coupling has attracted the attention of many scholars. The method of train coordination and anti‐collision control is not only the key to realize the virtual coupling of train, but also the key to ensure the safety of train operation. Therefore, based on the existing research, a virtual coupled train dynamics model with nonlinear dynamics is established. Then, the parameters of the operation process model of the nonlinear virtual coupled train are identified by the recursive least squares method based on real‐time data, which is applied to the variable parameter artificial potential field (VAPF) for parameter identification. A fusion controller based on feature‐based generalized model prediction (GPC) and VAPF is used to control the virtual coupled train and prevent collision. Finally, the validity of the proposed method is verified by using real high‐speed railway data.
In rail transit systems, improving transportation efficiency has become a research hotspot. In recent years, a new method of train control system based on virtual coupling has attracted the attention of many scholars. Virtual coupled train is a typical nonlinear system. How to control the nonlinear system with high precision is the key to realize the safe and efficient operation of virtual coupled train. Therefore, this paper proposes a virtual coupling scheme based on local leader–follower method. On this basis, the controller based on artificial potential field with variable parameter mixed is applied to carry out the collaborative and anti-collision control for the virtual coupled train system with nonlinear dynamics. Finally, a section of the Beijing–Shanghai high-speed railway is taken as the background to verify the effectiveness of artificial potential field with variable parameter mixed controller. The results show that the variable parameter artificial potential field controller with changing the weight can reduce the average error of the stop by 0.4304[Formula: see text]m compared with the traditional controller.
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