This paper presents a constrained Model Predictive Control (MPC) strategy enriched with soft-control techniques as neural networks and fuzzy logic, to incorporate self-tuning capabilities and reliability aspects for the management of drinking water networks (DWNs). The control system architecture consists in a multilayer controller with three hierarchical layers: learning and planning layer, supervision and adaptation layer, and feedback control layer. Results of applying the proposed approach to the Barcelona DWN show that the quasi-explicit nature of the proposed adaptive predictive controller leads to improve the computational time, especially when the complexity of the problem structure can vary while tuning the receding horizons. © 2013 Elsevier Ltd. ; This work has been partially supported by the Spanish research project WATMAN (CICYT DPI2009-13744) of the Science and Technology Ministry, the EU Project EFFINET (FP7-ICT-2011-8-318556) and the DGR of Generalitat de Catalunya (SAC group Ref. 2009/SGR/1491). ; Peer Reviewed
Learning-based tuning of supervisory model predictive control for drinking water networks
01.01.2013
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC: | 629 |
Learning-based tuning of supervisory model predictive control for drinking water networks
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