Por favor, use este identificador para citar o enlazar a este item:
http://hdl.handle.net/10261/96417
COMPARTIR / EXPORTAR:
SHARE CORE BASE | |
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE | |
Título: | Learning-based tuning of supervisory model predictive control for drinking water networks |
Autor: | Grosso, J. M.; Ocampo-Martínez, Carlos CSIC ORCID ; Puig, Vicenç CSIC ORCID | Palabras clave: | Multilayer controller Self-tuning Neural networks Drinking water networks Fuzzy-logic Model predictive control |
Fecha de publicación: | 2013 | Editor: | Elsevier | Citación: | Engineering Applications of Artificial Intelligence 26(7): 1741-1750 (2013) | Resumen: | 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. | Versión del editor: | http://dx.doi.org/10.1016/j.engappai.2013.03.003 | URI: | http://hdl.handle.net/10261/96417 | DOI: | 10.1016/j.engappai.2013.03.003 | Identificadores: | doi: 10.1016/j.engappai.2013.03.003 issn: 0952-1976 |
Aparece en las colecciones: | (IRII) Artículos |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
---|---|---|---|---|
Learning-based.pdf | 537,3 kB | Adobe PDF | Visualizar/Abrir |
CORE Recommender
SCOPUSTM
Citations
18
checked on 20-abr-2024
WEB OF SCIENCETM
Citations
15
checked on 23-feb-2024
Page view(s)
289
checked on 23-abr-2024
Download(s)
678
checked on 23-abr-2024
Google ScholarTM
Check
Altmetric
Altmetric
NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.