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Learning-based tuning of supervisory model predictive control for drinking water networks

Autor
Grosso, J.M.; Ocampo-Martinez, C.A.; Puig, V.
Tipus d'activitat
Article en revista
Revista
Engineering applications of artificial intelligence
Data de publicació
2013
Volum
26
Número
7
Pàgina inicial
1741
Pàgina final
1750
DOI
https://doi.org/10.1016/j.engappai.2013.03.003 Obrir en finestra nova
Projecte finançador
Análisis y diseño de estrategias de control óptimo distribuido aplicadas a la gestión de sistemas de agua de gran escala (WATMAN)
EFFICIENT INTEGRATED REAL TIME MONITORING AND CONTROL OF DRINKING WATER NETWORKS
Repositori
http://hdl.handle.net/2117/20298 Obrir en finestra nova
URL
http://www.sciencedirect.com/science/article/pii/S0952197613000390 Obrir en finestra nova
Resum
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 t...
Citació
Grosso, J.; Ocampo-Martinez, C.A.; Puig, V. Learning-based tuning of supervisory model predictive control for drinking water networks. "Engineering applications of artificial intelligence", 2013, vol. 26, núm. 7, p. 1741-1750.
Paraules clau
Drinking water networks, Fuzzy-logic, Model predictive control, Multilayer controller, Neural networks, Self-tuning
Grup de recerca
CS2AC-UPC - Supervision, Safety and Automatic Control
SAC - Sistemes Avançats de Control

Participants