@article{LIN2025, 
author = {Junting LIN and Mingjun NI},
title = {Adaptive model predictive control of virtual coupled based on artificial potential field},
year = {2025},
journal = {Journal of Beijing University of Aeronautics and Astronautics},
volume = {51},
number = {10},
pages = {3273-3285},
keywords = {virtually coupled, model predictive control, artificial potential field, genetic algorithm, train tracking operation optimization},
url = {https://www.sciopen.com/article/10.13700/j.bh.1001-5965.2023.0544},
doi = {10.13700/j.bh.1001-5965.2023.0544},
abstract = {The safety of operating formation trains is now subject to stricter standards due to the high speed and high density tracking control of trains. To meet the need for precision and adaptability during train operation, an adaptive model predictive control (MPC) technique based on an artificial potential field is created for virtual coupled (VC) systems. First, a VC train is used as the research object, and the MPC method is used to create a dynamic model based on the equilibrium state of the train, with control accuracy and smoothness as the optimization objectives. Next, a collision avoidance function based on an artificial potential field setting is added to the objective function, allowing for the realization of the formation’s collision avoidance control. In order to improve the real-time performance of the system and ensure accurate control of the train formation, it is also necessary to analyze the impact of various time domain parameters on the control accuracy and computational efficiency of the system, design the corresponding adaptation function, and base this on a genetic algorithm (GA) that can find the best combinations of time domain parameters under various working conditions. On the MATLAB platform, a 4-train tracking operation scenario is developed as a last step to test the effectiveness of the suggested approach. The study’s findings demonstrate that the artificial potential field-based model predictive controller has interval control accuracy that is 94.8% greater than that of the conventional model predictive controller, successfully preventing train collisions and ensuring the safety of train operation. Furthermore, the adaptive control law controller may adjust the system in real-time based on the train’s operational status, resulting in a 10% increase in computing efficiency and excellent control accuracy. The study’s findings support the viability of the suggested control approach, enhance the controller’s overall control performance, and serve as a guide for future improvements in formation control and ensuring safe train operation.}
}