Abstract
Automatic train operation is an important part of the train control system. As train operating intervals continue to shorten and train speeds continue to increase, multiple train cooperative control is currently an important technology to further improve the efficiency of train operation and line passing capacity. However, considering various factors such as the nonlinearity and uncertainty of the train dynamics model and the complexity of the line conditions, this creates even greater demands on the design of the controller. In this study, we propose an adaptive cooperative tracking control method for multiple trains using adjacent information. For the multiple-train coordinated tracking control in the presence of model uncertainties, unknown parameters, and external disturbances, a distributed cooperative control scheme for multiple trains is designed using the displacement, velocity, and acceleration information of adjacent trains, combined with radial basis function neural networks and adaptive methods. A fast high-order sliding mode observer is used to estimate the train velocity and acceleration information. Stability and convergence are proved for single and multiple trains utilizing Lyapunov stability analysis. Simulation examples demonstrate the effectiveness of the proposed controller.
Highlights
With its safety, efficiency and low carbon footprint, urban railway transportation are becoming an indispensable mode of transport in the development of major cities [1]
Motivated by these above observations, this study proposes an adaptive cooperative tracking control method for multiple trains using adjacent information
For the train dynamics model (3), considering that the basic resistance and combined disturbances of the trains are unknown, the multiple trains cooperative control law is designed with the following control objectives: to ensure that the displacement and velocity of the first train can follow the desired trajectory, that the displacement of the following train can maintain a pre-specified distance from the preceding train while the velocity remains consistent, and that the closed-loop signals for all trains are guaranteed to be uniformly bounded
Summary
Efficiency and low carbon footprint, urban railway transportation are becoming an indispensable mode of transport in the development of major cities [1]. In [22], a fuzzy gain-scheduling sliding mode control method is proposed by combining sliding mode control with fuzzy logic, solving the attitude regulation problem of unmanned quadcopters with parameter uncertainty and external disturbances Motivated by these above observations, this study proposes an adaptive cooperative tracking control method for multiple trains using adjacent information. The contributions of this study are presented as follows: (1) For the multiple trains coordinated tracking control in the presence of model uncertainties, unknown parameters and external disturbances, a distributed cooperative control scheme for multiple trains is designed using the displacement, velocity, and acceleration information of adjacent trains, combined with radial basis function neural networks and adaptive methods.
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