Hierarchical Model Predictive Control for On-Line High-Speed Railway Delay Management and Train Control in a Dynamic Operations Environment

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 article, a novel two-layer hierarchical model predictive control (MPC) model is proposed for on-line high-sp...

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Veröffentlicht in:IEEE transactions on control systems technology Jg. 30; H. 6; S. 2344 - 2359
Hauptverfasser: Wang, Yihui, Zhu, Songwei, Li, Shukai, Yang, Lixing, De Schutter, Bart
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Hierarchical model predictive control (MPC)
High speed rail
High-speed rail transportation
high-speed railway
Integer programming
Linear programming
Mixed integer
Mixed integer linear programming
mixed integer linear programming (MILP)
Optimization
Predictive control
Real-time systems
Scheduling
Speed control
train rescheduling
Velocity control
Weather
ISSN:1063-6536, 1558-0865
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Zusammenfassung: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 article, 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, that is, 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, for example, 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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content type line 14
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2022.3140805