Reliability-based economic model predictive control for generalised flow-based networks including actuators' health-aware capabilities

Juan M. Grosso; Carlos Ocampo-Martinez; Vicenç Puig

International Journal of Applied Mathematics and Computer Science (2016)

  • Volume: 26, Issue: 3, page 641-654
  • ISSN: 1641-876X

Abstract

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This paper proposes a reliability-based economic model predictive control (MPC) strategy for the management of generalised flow-based networks, integrating some ideas on network service reliability, dynamic safety stock planning, and degradation of equipment health. The proposed strategy is based on a single-layer economic optimisation problem with dynamic constraints, which includes two enhancements with respect to existing approaches. The first enhancement considers chance-constraint programming to compute an optimal inventory replenishment policy based on a desired risk acceptability level, leading to dynamical allocation of safety stocks in flow-based networks to satisfy non-stationary flow demands. The second enhancement computes a smart distribution of the control effort and maximises actuators' availability by estimating their degradation and reliability. The proposed approach is illustrated with an application of water transport networks using the Barcelona network as the case study considered.

How to cite

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Juan M. Grosso, Carlos Ocampo-Martinez, and Vicenç Puig. "Reliability-based economic model predictive control for generalised flow-based networks including actuators' health-aware capabilities." International Journal of Applied Mathematics and Computer Science 26.3 (2016): 641-654. <http://eudml.org/doc/286725>.

@article{JuanM2016,
abstract = {This paper proposes a reliability-based economic model predictive control (MPC) strategy for the management of generalised flow-based networks, integrating some ideas on network service reliability, dynamic safety stock planning, and degradation of equipment health. The proposed strategy is based on a single-layer economic optimisation problem with dynamic constraints, which includes two enhancements with respect to existing approaches. The first enhancement considers chance-constraint programming to compute an optimal inventory replenishment policy based on a desired risk acceptability level, leading to dynamical allocation of safety stocks in flow-based networks to satisfy non-stationary flow demands. The second enhancement computes a smart distribution of the control effort and maximises actuators' availability by estimating their degradation and reliability. The proposed approach is illustrated with an application of water transport networks using the Barcelona network as the case study considered.},
author = {Juan M. Grosso, Carlos Ocampo-Martinez, Vicenç Puig},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {model predictive control; flow-based networks; dynamic safety stocks; actuator health; service reliability; chance constraints; economic optimisation},
language = {eng},
number = {3},
pages = {641-654},
title = {Reliability-based economic model predictive control for generalised flow-based networks including actuators' health-aware capabilities},
url = {http://eudml.org/doc/286725},
volume = {26},
year = {2016},
}

TY - JOUR
AU - Juan M. Grosso
AU - Carlos Ocampo-Martinez
AU - Vicenç Puig
TI - Reliability-based economic model predictive control for generalised flow-based networks including actuators' health-aware capabilities
JO - International Journal of Applied Mathematics and Computer Science
PY - 2016
VL - 26
IS - 3
SP - 641
EP - 654
AB - This paper proposes a reliability-based economic model predictive control (MPC) strategy for the management of generalised flow-based networks, integrating some ideas on network service reliability, dynamic safety stock planning, and degradation of equipment health. The proposed strategy is based on a single-layer economic optimisation problem with dynamic constraints, which includes two enhancements with respect to existing approaches. The first enhancement considers chance-constraint programming to compute an optimal inventory replenishment policy based on a desired risk acceptability level, leading to dynamical allocation of safety stocks in flow-based networks to satisfy non-stationary flow demands. The second enhancement computes a smart distribution of the control effort and maximises actuators' availability by estimating their degradation and reliability. The proposed approach is illustrated with an application of water transport networks using the Barcelona network as the case study considered.
LA - eng
KW - model predictive control; flow-based networks; dynamic safety stocks; actuator health; service reliability; chance constraints; economic optimisation
UR - http://eudml.org/doc/286725
ER -

References

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