Model Predictive Control for rail condition-based maintenance

A multilevel approach

Conference paper (2016)

Authors

Z. Su Team Bart De Schutter - Mechanical, Maritime and Materials Engineering
Alfredo Nunez Railway Engineering - CEG
S. Baldi Team Bart De Schutter - Mechanical, Maritime and Materials Engineering
B.H.K. De Schutter Team Bart De Schutter - Mechanical, Maritime and Materials Engineering
Department
Delft Center for Systems and Control
Degradation Uncertainty Planning Decision making Rails Rail transportation Maintenance engineering
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Published Date

2016

Language

English

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Department

Delft Center for Systems and Control

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.