Reference
Z. Su, A. Núñez, S. Baldi, and B. De Schutter, "Model predictive
control for rail condition-based maintenance: A multilevel approach,"
Proceedings of the 2016 IEEE 19th International Conference on
Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil, pp.
354-359, Nov. 2016.
Abstract
This paper develops a multilevel decision making approach based on model
predictive control (MPC) for condition-based maintenance of rail. We address a
typical railway surface defect called "squat", in which three maintenance
actions can be considered: no maintenance, grinding, and replacement. A
scenario-based scheme is applied to address the uncertainty in the
deterioration dynamics of the key performance indicator for each track section,
and a piecewise-affine model is used to approximate the expected dynamics,
which is to be optimized by a scenario-based MPC controller at the high level.
A static optimization problem involving clustering and mixed integer linear
programming is solved at the low level to produce an efficient grinding and
replacing schedule. A case study using real measurements obtained from a Dutch
railway line between Eindhoven and Weert is performed to demonstrate the merits
of the proposed approach.
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BibTeX
@inproceedings{SuNun:16-022,
author = {Su, Zhou and N{\'{u}}{\~{n}}ez, Alfredo and Baldi, Simone and
De Schutter, Bart},
title = {Model Predictive Control for Rail Condition-Based Maintenance:
{A} Multilevel Approach},
booktitle = {Proceedings of the 2016 IEEE 19th International Conference on
Intelligent Transportation Systems (ITSC)},
address = {Rio de Janeiro, Brazil},
pages = {354--359},
month = nov,
year = {2016}
}