Reference
Y. Wang, S. Zhu, S. Li, L. Yang, and B. De Schutter, "Hierarchical model
predictive control for on-line high-speed railway delay management and train
control in a dynamic operations environment,"
IEEE
Transactions on Control Systems Technology, vol. 30, no. 6, pp.
2344-2359, Nov. 2022.
Abstract
In practice, the operation of high-speed trains is often affected by adverse
weather conditions or equipment failures, which result in delays and even
cancellations of train services. In this paper, a novel two-layer hierarchical
model predictive control (MPC) model is proposed for on-line high-speed railway
delay management and train control for minimizing train delays and
cancellations. The upper layer manages the global objectives of the train
operation, i.e., minimizing the total train delays and providing guidance for
the speed control in the lower layer. The objectives of the lower layer are to
satisfy the running time requirements given by the upper layer and to save
energy at the same time. The optimization problems in both levels of the
hierarchical MPC framework are formulated as small-scale mixed integer linear
programming problems, which can be solved efficiently by existing solvers.
Particularly, the train control problem is solved in a distributed way for each
train. Simulation analysis based on the real-life data of the Beijing-Shanghai
high-speed railway shows that the proposed hierarchical MPC framework can meet
the real-time requirements and reduce train delays effectively when compared
with widely accepted strategies, e.g., first-scheduled-first-serve and
first-come-first-serve. Moreover, the proposed hierarchical MPC framework also
provides good robustness performance for different disturbance scenarios.
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BibTeX
@article{WanZhu:22-017,
author = {Wang, Yihui and Zhu, Songwei and Li, Shukai and Yang, Lixing and
De Schutter, Bart},
title = {Hierarchical Model Predictive Control for On-Line High-Speed
Railway Delay Management and Train Control in a Dynamic
Operations Environment},
journal = {IEEE Transactions on Control Systems Technology},
volume = {30},
number = {6},
pages = {2344--2359},
month = nov,
year = {2022}
}