TR2015-063
A Framework for Real-time Near-optimal Train Run-curve Computation with Dynamic Travel Time and Speed Limits
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- "A Framework for Real-Time Near-Optimal Train Run-Curve Computation with Dynamic Travel Time and Speed Limits", American Control Conference (ACC), DOI: 10.1109/ACC.2015.7170790, July 2015, pp. 533-540.BibTeX TR2015-063 PDF
- @inproceedings{Xu2015jul,
- author = {Xu, J. and Nikovski, D.N.},
- title = {A Framework for Real-Time Near-Optimal Train Run-Curve Computation with Dynamic Travel Time and Speed Limits},
- booktitle = {American Control Conference (ACC)},
- year = 2015,
- pages = {533--540},
- month = jul,
- publisher = {IEEE},
- doi = {10.1109/ACC.2015.7170790},
- isbn = {978-1-4799-8685-9},
- url = {https://www.merl.com/publications/TR2015-063}
- }
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- "A Framework for Real-Time Near-Optimal Train Run-Curve Computation with Dynamic Travel Time and Speed Limits", American Control Conference (ACC), DOI: 10.1109/ACC.2015.7170790, July 2015, pp. 533-540.
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Abstract:
This paper studies the problem to generate the most energy efficient run-curves subject to given travel time requirements. The target is to provide a train with the ability to quickly adjust its run curve according to different travel time requirements and speed limits along the track before departing a terminal. Using a train model considering train length, varying track gradient and speed limit profile, the optimal run-curve problem is formulated into a bi-criteria optimization problem that minimizes weighted energy consumption and weighted travel time. By selecting appropriate weight values, the optimization problem would generate a run-curve with near-optimal energy consumption. We propose a two stage procedure framework, which includes an off-line stage and a real-time stage. A series of geometric relation between weight in the objective function and travel time are derived. The actual run-curves are generated in the real-time stage using approximate dynamic programming. For the first time, a framework provides trains with the ability to fast response to dynamic travel time requirement using complex physical models.
Related News & Events
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NEWS MERL researchers present 10 papers at the American Controls Conference Date: July 3, 2015
MERL Contacts: Daniel N. Nikovski; Yebin Wang; Stefano Di Cairano; Arvind Raghunathan; Avishai WeissBrief- MERL researchers presented 10 papers at the American Controls Conference, in Chicago, USA. The ACC is one of the most important conferences on control systems in the world. Topics ranged from theoretical, including new algorithms for Model Predictive Control and Co-Design, to applications including spacecraft control and HVAC systems.