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...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Grosso, J.M. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013transfer abstract |
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| Schlagwörter: |
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| Umfang: |
10 |
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| Übergeordnetes Werk: |
Enthalten in: Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation - Liu, Xiang ELSEVIER, 2015, the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:26 ; year:2013 ; number:7 ; pages:1741-1750 ; extent:10 |
| Links: |
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| DOI / URN: |
10.1016/j.engappai.2013.03.003 |
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ELV038896583 |
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| 520 | |a 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. | ||
| 520 | |a 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. | ||
| 650 | 7 | |a Model predictive control |2 Elsevier | |
| 650 | 7 | |a Self-tuning |2 Elsevier | |
| 650 | 7 | |a Neural networks |2 Elsevier | |
| 650 | 7 | |a Multilayer controller |2 Elsevier | |
| 650 | 7 | |a Fuzzy-logic |2 Elsevier | |
| 650 | 7 | |a Drinking water networks |2 Elsevier | |
| 700 | 1 | |a Ocampo-Martínez, C. |4 oth | |
| 700 | 1 | |a Puig, V. |4 oth | |
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10.1016/j.engappai.2013.03.003 doi GBVA2013015000007.pica (DE-627)ELV038896583 (ELSEVIER)S0952-1976(13)00039-0 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Grosso, J.M. verfasserin aut Learning-based tuning of supervisory model predictive control for drinking water networks 2013transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Model predictive control Elsevier Self-tuning Elsevier Neural networks Elsevier Multilayer controller Elsevier Fuzzy-logic Elsevier Drinking water networks Elsevier Ocampo-Martínez, C. oth Puig, V. oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:26 year:2013 number:7 pages:1741-1750 extent:10 https://doi.org/10.1016/j.engappai.2013.03.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 26 2013 7 1741-1750 10 045F 004 |
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10.1016/j.engappai.2013.03.003 doi GBVA2013015000007.pica (DE-627)ELV038896583 (ELSEVIER)S0952-1976(13)00039-0 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Grosso, J.M. verfasserin aut Learning-based tuning of supervisory model predictive control for drinking water networks 2013transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Model predictive control Elsevier Self-tuning Elsevier Neural networks Elsevier Multilayer controller Elsevier Fuzzy-logic Elsevier Drinking water networks Elsevier Ocampo-Martínez, C. oth Puig, V. oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:26 year:2013 number:7 pages:1741-1750 extent:10 https://doi.org/10.1016/j.engappai.2013.03.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 26 2013 7 1741-1750 10 045F 004 |
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10.1016/j.engappai.2013.03.003 doi GBVA2013015000007.pica (DE-627)ELV038896583 (ELSEVIER)S0952-1976(13)00039-0 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Grosso, J.M. verfasserin aut Learning-based tuning of supervisory model predictive control for drinking water networks 2013transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Model predictive control Elsevier Self-tuning Elsevier Neural networks Elsevier Multilayer controller Elsevier Fuzzy-logic Elsevier Drinking water networks Elsevier Ocampo-Martínez, C. oth Puig, V. oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:26 year:2013 number:7 pages:1741-1750 extent:10 https://doi.org/10.1016/j.engappai.2013.03.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 26 2013 7 1741-1750 10 045F 004 |
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10.1016/j.engappai.2013.03.003 doi GBVA2013015000007.pica (DE-627)ELV038896583 (ELSEVIER)S0952-1976(13)00039-0 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Grosso, J.M. verfasserin aut Learning-based tuning of supervisory model predictive control for drinking water networks 2013transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Model predictive control Elsevier Self-tuning Elsevier Neural networks Elsevier Multilayer controller Elsevier Fuzzy-logic Elsevier Drinking water networks Elsevier Ocampo-Martínez, C. oth Puig, V. oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:26 year:2013 number:7 pages:1741-1750 extent:10 https://doi.org/10.1016/j.engappai.2013.03.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 26 2013 7 1741-1750 10 045F 004 |
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10.1016/j.engappai.2013.03.003 doi GBVA2013015000007.pica (DE-627)ELV038896583 (ELSEVIER)S0952-1976(13)00039-0 DE-627 ger DE-627 rakwb eng 004 004 DE-600 540 VZ 610 VZ 44.00 bkl Grosso, J.M. verfasserin aut Learning-based tuning of supervisory model predictive control for drinking water networks 2013transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier 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. 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. Model predictive control Elsevier Self-tuning Elsevier Neural networks Elsevier Multilayer controller Elsevier Fuzzy-logic Elsevier Drinking water networks Elsevier Ocampo-Martínez, C. oth Puig, V. oth Enthalten in Elsevier Science Liu, Xiang ELSEVIER Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation 2015 the international journal of real-time automation : a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV013402978 volume:26 year:2013 number:7 pages:1741-1750 extent:10 https://doi.org/10.1016/j.engappai.2013.03.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.00 Medizin: Allgemeines VZ AR 26 2013 7 1741-1750 10 045F 004 |
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Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation |
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Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation |
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Learning-based tuning of supervisory model predictive control for drinking water networks |
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Learning-based tuning of supervisory model predictive control for drinking water networks |
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Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation |
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Copper oxide nanomaterials synthesized from simple copper salts as active catalysts for electrocatalytic water oxidation |
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learning-based tuning of supervisory model predictive control for drinking water networks |
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Learning-based tuning of supervisory model predictive control for drinking water networks |
| abstract |
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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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. |
| abstract_unstemmed |
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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Learning-based tuning of supervisory model predictive control for drinking water networks |
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