TR2020-087
A Reference Governor for Wheel-Slip Prevention in Railway Vehicles with Pneumatic Brakes
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- "A Reference Governor for Wheel-Slip Prevention in Railway Vehicles with Pneumatic Brakes", American Control Conference (ACC), DOI: 10.23919/ACC45564.2020.9147647, June 2020.BibTeX TR2020-087 PDF
- @inproceedings{Danielson2020jun,
- author = {Danielson, Claus and Di Cairano, Stefano},
- title = {A Reference Governor for Wheel-Slip Prevention in Railway Vehicles with Pneumatic Brakes},
- booktitle = {American Control Conference (ACC)},
- year = 2020,
- month = jun,
- publisher = {IEEE},
- doi = {10.23919/ACC45564.2020.9147647},
- url = {https://www.merl.com/publications/TR2020-087}
- }
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- "A Reference Governor for Wheel-Slip Prevention in Railway Vehicles with Pneumatic Brakes", American Control Conference (ACC), DOI: 10.23919/ACC45564.2020.9147647, June 2020.
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Abstract:
This paper applies reference governor (RG) design to the problem of preventing excessive wheel-slip in railway vehicles with pneumatic brakes. The RG minimizes the difference between the desired and implemented deceleration set-point such that the system state remains inside a constraint admissible positive invariant set where wheel-slip is maintained below a prescribed level. This problem is complicated by the non-linear slip-dynamics and hysteresis in the pneumatic brake which results in a non-convex invariant set. The RG is evaluated in numerical simulations where we observe that the governor produces non-linear integral-action that has the beneficial properties of fast transient response and offset-free tracking while being robust to delays from hysteresis and uncertainty on the slip dynamics.
Related News & Events
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NEWS MERL researchers presented 10 papers at American Control Conference (ACC) Date: July 1, 2020 - July 3, 2020
Where: Denver, Colorado (virtual)
MERL Contacts: Mouhacine Benosman; Ankush Chakrabarty; Stefano Di Cairano; Yebin Wang; Avishai Weiss
Research Areas: Control, Machine Learning, OptimizationBrief- At the American Control Conference, MERL presented 10 papers on subjects including autonomous-vehicle decision making and motion planning, nonlinear estimation for thermal-fluid models and GNSS positioning, learning-based reference governors and reference governors for railway vehicles, and fail-safe rendezvous control.