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In order to control the nonlinear high-speed train with high robustness, the fractional order control of nonlinear switching systems is studied. The fractional order controller is designed for a class of nonlinear switching systems by the fractional order backstepping method. In this paper, a simple and effective online updating scheme of model coefficients is proposed by using the flexibility of the model predictive control algorithm and its wide range of model accommodation. A stochastic discrete nonlinear state space model describing the mechanical behavior of a single particle in a high-speed train is constructed, and the maximum likelihood estimation of the parameters of a high-speed train is transformed into an optimization problem with great expectations. Finally, numerical comparison experiments of motion characters of two high-speed trains are given. The results show the effectiveness of the proposed identification method.

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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.