<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Reliability–based economic model predictive control for generalised flow–based networks including actuators’ health–aware capabilities

Automatic Control Department, Institute of Robotics and Industrial Informatics (CSIC-UPC), Polytechnic University of Catalonia (UPC), Llorens and Artigas 4–6, 08028 Barcelona,

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper proposes a reliability-based economic model predictive control (MPC) strategy for the management of generalised flow-based networks, integrating...

Reliability–based economic model predictive control for generalised flow–based networks including actuators’ health–aware capabilities

Publié en ligne: 29 sept. 2016

Pages: 641 - 654

Reçu: 13 avr. 2015

Accepté: 16 févr. 2016

DOI: https://doi.org/10.1515/amcs-2016-0044

Mots clés
model predictive control, flow-based networks, dynamic safety stocks, actuator health, service reliability, chance constraints, economic optimisation

© 2016 Juan M. Grosso et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Reliability–based economic model predictive control for generalised flow–based networks including actuators’ health–aware capabilities

Publié en ligne: 29 sept. 2016

Pages: 641 - 654

Reçu: 13 avr. 2015

Accepté: 16 févr. 2016

DOI: https://doi.org/10.1515/amcs-2016-0044

Mots clésmodel predictive control, flow-based networks, dynamic safety stocks, actuator health, service reliability, chance constraints, economic optimisation

© 2016 Juan M. Grosso et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
model predictive control, flow-based networks, dynamic safety stocks, actuator health, service reliability, chance constraints, economic optimisation