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. ; Peer Reviewed ; Preprint
Learning-based tuning of supervisory model predictive control for drinking water networks
01.01.2013
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Multilayer controller , Aigua potable -- Abastament -- Control automàtic , Àrees temàtiques de la UPC::Informàtica::Automàtica i control , marítima i sanitària::Enginyeria sanitària , Drinking water networks , Àrees temàtiques de la UPC::Enginyeria civil::Enginyeria hidràulica , Fuzzy-logic , Neural networks , Drinking water -- Spain -- Barcelona , Model predictive control , Self-tuning
DDC: | 629 |
Learning-based tuning of supervisory model predictive control for drinking water networks
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