Research on Operation Curve Planning Technology Adaptive to Undulating Tracks for Heavy-Haul Trains
The operation of heavy-haul trains is susceptible to train impulses and excessive energy consumption due to improper control, which can lead to safety incidents such as coupler jumping and breaking. These issues arise from their long formations and heavy loads, as well as the complex longitudinal pr...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Control and Information Technology
2024-08-01
|
| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2024.04.004 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The operation of heavy-haul trains is susceptible to train impulses and excessive energy consumption due to improper control, which can lead to safety incidents such as coupler jumping and breaking. These issues arise from their long formations and heavy loads, as well as the complex longitudinal profiles of heavy-haul railways. In order to explore an optimal control strategy for heavy-haul trains running on undulating tracks, this paper proposes a curve planning algorithm based on the ant colony algorithm for sectional operation. Firstly, a multi-mass model of train longitudinal dynamics was established, and Zhai's method was applied to determine the model's state variables. Secondly, from the perspective of coupler forces, a safety evaluation index based on coupler force constraints and a stability evaluation index based on changes in coupler force states were established. By integrating the actual operational constraints of heavy-haul trains, a multi-objective optimization mathematical model with constraints was constructed. Finally, the ant colony algorithm was used to solve this model, resulting in the generation of the optimal control curve for heavy-haul trains running on undulating track sections. Based on actual track data, the proposed algorithm was verified through simulations. The results showed that, compared with operation by highly skilled drivers, the algorithm reduced the number of coupler state transitions and the slope index of coupler force changes by more than 3% on average, demonstrating an effective improvement in operational quality for heavy-haul trains. |
|---|---|
| ISSN: | 2096-5427 |