Distributed chance-constrained model predictive control for condition-based maintenance planning for railway infrastructures

Distributed chance-constrained model predictive control for condition-based maintenance planning for railway infrastructures

Su, Z. (TU Delft Team Bart De Schutter)
Jamshidi, A. (TU Delft Railway Engineering)
Nunez, Alfredo (TU Delft Railway Engineering)
Baldi, S. (TU Delft Team Bart De Schutter)
De Schutter, B.H.K. (TU Delft Delft Center for Systems and Control; TU Delft Team Bart De Schutter)

Lughofer, Edwin (editor)
Sayed-Mouchaweh, Moamar (editor)

Delft Center for Systems and Control

2019

We develop a Model Predictive Control (MPC) approach for condition-based maintenance planning under uncertainty for railway infrastructure systems composed of multiple components. Piecewise-affine models with uncertain parameters are used to capture both the nonlinearity and uncertainties in the deterioration process. To keep a balance between robustness and optimality, we formulate the MPC optimization problem as a chance-constrained problem, which ensures that the constraints, e.g., bounds on the degradation level, are satisfied with a given probabilistic guarantee. Two distributed algorithms, one based on Dantzig-Wolfe decomposition and the other derived from a constraint-tightening technique, are proposed to improve the scalability of the MPC approach. Computational experiments show that the distributed method based on Dantzig-Wolfe decomposition performs the best in terms of computational time and convergence to global optimality. By comparing the chance-constrained MPC approaches with deterministic approach, and traditional time-based maintenance approach, we show that despite their high computational requirements, chance-constrained MPC approaches are cost-efficient and robust in the presence of uncertainties.

http://resolver.tudelft.nl/uuid:1a03576e-560c-4c81-962f-84b11bac6813

https://doi.org/10.1007/978-3-030-05645-2_18

Springer, Cham, Switzerland

2019-09-01

978-3-030-05644-5

Predictive Maintenance in Dynamic Systems: Advanced Methods, Decision Support Tools and Real-World Applications

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

book chapter

© 2019 Z. Su, A. Jamshidi, Alfredo Nunez, S. Baldi, B.H.K. De Schutter