Learning-based tuning of supervisory model predictive control for drinking water networks
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 co...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2013 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/20298 |
| Acceso en línea: | https://hdl.handle.net/2117/20298 https://dx.doi.org/10.1016/j.engappai.2013.03.003 |
| Access Level: | acceso abierto |
| Palabra clave: | Drinking water networks Drinking water -- Spain -- Barcelona Fuzzy-logic Model predictive control Multilayer controller Neural networks Self-tuning Aigua potable -- Abastament -- Control automàtic Àrees temàtiques de la UPC::Informàtica::Automàtica i control Àrees temàtiques de la UPC::Enginyeria civil::Enginyeria hidràulica, marítima i sanitària::Enginyeria sanitària |
| Sumario: | 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. |
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