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Research on Train Dynamic Coupling Strategy Based on Distributed Model Predictive Control
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DOI：10.1088/1742-6596/2183/1/012029.png)
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Abstract
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Abstract
This paper mainly studies the problem of dynamic coupling under multiple optimization objectives and multiple constraints. Virtual coupling (VC), as a new train operation mode, effectively improves the operating efficiency of the line, but reduces the safety margin between trains, and requires higher control accuracy. As an important scenario of virtual coupling, dynamic formation has not formed a set of applicable train control strategies. Aiming at the dynamic train formation scenario of virtual coupling, propose a control architecture based on distributed model predictive control (DMPC). Numerical simulation using Matlab verifies the effectiveness of the algorithm in this paper. The simulation results show that the train is under the condition of satisfying the operating constraints. Compared with PID control, the method in this paper is more effective and more accurate.
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