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Título:
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Learning-based tuning of supervisory model predictive control for drinking water networks
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Autor/a:
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Grosso Pérez, Juan Manuel; Ocampo-Martínez, Carlos; Puig Cayuela, Vicenç
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Otros autores:
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Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial; Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control |
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Abstract:
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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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Abstract:
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Peer Reviewed |
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Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Automàtica i control
-Àrees temàtiques de la UPC::Enginyeria civil::Enginyeria hidràulica, marítima i sanitària::Enginyeria sanitària
-Drinking water networks
-Drinking water -- Spain -- Barcelona
-Drinking water networks
-Fuzzy-logic
-Model predictive control
-Multilayer controller
-Neural networks
-Self-tuning
-Aigua potable -- Abastament -- Control automàtic |
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Derechos:
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Tipo de documento:
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Artículo - Borrador
Artículo |
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