Robust discrete-time set-based adaptive predictive control for nonlinear systems
• This paper proposes a robust adaptive nonlinear model predictive control design techniques for discrete-time nonlinear control systems. • A new set-based adaptive estimation routine is proposed to estimate the unknown parameters. • A proof of robust stability is presented for a minmax robust adapt...
Ausführliche Beschreibung
Autor*in: |
Gonçalves, Guilherme A.A. [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2016 |
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Schlagwörter: |
Nonlinear model predictive control |
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Umfang: |
12 |
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Übergeordnetes Werk: |
Enthalten in: A metric to gauge local distortion in metallic glasses and supercooled liquids - Wu, Chen ELSEVIER, 2014transfer abstract, a journal affiliated with IFAC, the International Federation of Automatic Control, Amsterdam [u.a.] |
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Übergeordnetes Werk: |
volume:39 ; year:2016 ; pages:111-122 ; extent:12 |
Links: |
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DOI / URN: |
10.1016/j.jprocont.2015.12.006 |
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Katalog-ID: |
ELV035434767 |
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520 | |a • This paper proposes a robust adaptive nonlinear model predictive control design techniques for discrete-time nonlinear control systems. • A new set-based adaptive estimation routine is proposed to estimate the unknown parameters. • A proof of robust stability is presented for a minmax robust adaptive nonlinear model predictive control framework. • A Lipschitz-based approximation approach is used to remove the need for a minmax formulation. • A nonisothermal CSTR problem and a chemotherapy control problem are effectively solved with the design methodology. | ||
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10.1016/j.jprocont.2015.12.006 doi GBVA2016016000002.pica (DE-627)ELV035434767 (ELSEVIER)S0959-1524(15)00240-1 DE-627 ger DE-627 rakwb eng 004 004 DE-600 670 VZ 330 VZ Gonçalves, Guilherme A.A. verfasserin aut Robust discrete-time set-based adaptive predictive control for nonlinear systems 2016 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • This paper proposes a robust adaptive nonlinear model predictive control design techniques for discrete-time nonlinear control systems. • A new set-based adaptive estimation routine is proposed to estimate the unknown parameters. • A proof of robust stability is presented for a minmax robust adaptive nonlinear model predictive control framework. • A Lipschitz-based approximation approach is used to remove the need for a minmax formulation. • A nonisothermal CSTR problem and a chemotherapy control problem are effectively solved with the design methodology. Nonlinear model predictive control Elsevier Adaptive model predictive control Elsevier Robust model predictive control Elsevier Guay, Martin oth Enthalten in Elsevier Science Wu, Chen ELSEVIER A metric to gauge local distortion in metallic glasses and supercooled liquids 2014transfer abstract a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV022993630 volume:39 year:2016 pages:111-122 extent:12 https://doi.org/10.1016/j.jprocont.2015.12.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 GBV_ILN_73 AR 39 2016 111-122 12 045F 004 |
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10.1016/j.jprocont.2015.12.006 doi GBVA2016016000002.pica (DE-627)ELV035434767 (ELSEVIER)S0959-1524(15)00240-1 DE-627 ger DE-627 rakwb eng 004 004 DE-600 670 VZ 330 VZ Gonçalves, Guilherme A.A. verfasserin aut Robust discrete-time set-based adaptive predictive control for nonlinear systems 2016 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • This paper proposes a robust adaptive nonlinear model predictive control design techniques for discrete-time nonlinear control systems. • A new set-based adaptive estimation routine is proposed to estimate the unknown parameters. • A proof of robust stability is presented for a minmax robust adaptive nonlinear model predictive control framework. • A Lipschitz-based approximation approach is used to remove the need for a minmax formulation. • A nonisothermal CSTR problem and a chemotherapy control problem are effectively solved with the design methodology. Nonlinear model predictive control Elsevier Adaptive model predictive control Elsevier Robust model predictive control Elsevier Guay, Martin oth Enthalten in Elsevier Science Wu, Chen ELSEVIER A metric to gauge local distortion in metallic glasses and supercooled liquids 2014transfer abstract a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV022993630 volume:39 year:2016 pages:111-122 extent:12 https://doi.org/10.1016/j.jprocont.2015.12.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 GBV_ILN_73 AR 39 2016 111-122 12 045F 004 |
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10.1016/j.jprocont.2015.12.006 doi GBVA2016016000002.pica (DE-627)ELV035434767 (ELSEVIER)S0959-1524(15)00240-1 DE-627 ger DE-627 rakwb eng 004 004 DE-600 670 VZ 330 VZ Gonçalves, Guilherme A.A. verfasserin aut Robust discrete-time set-based adaptive predictive control for nonlinear systems 2016 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • This paper proposes a robust adaptive nonlinear model predictive control design techniques for discrete-time nonlinear control systems. • A new set-based adaptive estimation routine is proposed to estimate the unknown parameters. • A proof of robust stability is presented for a minmax robust adaptive nonlinear model predictive control framework. • A Lipschitz-based approximation approach is used to remove the need for a minmax formulation. • A nonisothermal CSTR problem and a chemotherapy control problem are effectively solved with the design methodology. Nonlinear model predictive control Elsevier Adaptive model predictive control Elsevier Robust model predictive control Elsevier Guay, Martin oth Enthalten in Elsevier Science Wu, Chen ELSEVIER A metric to gauge local distortion in metallic glasses and supercooled liquids 2014transfer abstract a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV022993630 volume:39 year:2016 pages:111-122 extent:12 https://doi.org/10.1016/j.jprocont.2015.12.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 GBV_ILN_73 AR 39 2016 111-122 12 045F 004 |
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10.1016/j.jprocont.2015.12.006 doi GBVA2016016000002.pica (DE-627)ELV035434767 (ELSEVIER)S0959-1524(15)00240-1 DE-627 ger DE-627 rakwb eng 004 004 DE-600 670 VZ 330 VZ Gonçalves, Guilherme A.A. verfasserin aut Robust discrete-time set-based adaptive predictive control for nonlinear systems 2016 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • This paper proposes a robust adaptive nonlinear model predictive control design techniques for discrete-time nonlinear control systems. • A new set-based adaptive estimation routine is proposed to estimate the unknown parameters. • A proof of robust stability is presented for a minmax robust adaptive nonlinear model predictive control framework. • A Lipschitz-based approximation approach is used to remove the need for a minmax formulation. • A nonisothermal CSTR problem and a chemotherapy control problem are effectively solved with the design methodology. Nonlinear model predictive control Elsevier Adaptive model predictive control Elsevier Robust model predictive control Elsevier Guay, Martin oth Enthalten in Elsevier Science Wu, Chen ELSEVIER A metric to gauge local distortion in metallic glasses and supercooled liquids 2014transfer abstract a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV022993630 volume:39 year:2016 pages:111-122 extent:12 https://doi.org/10.1016/j.jprocont.2015.12.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 GBV_ILN_73 AR 39 2016 111-122 12 045F 004 |
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• This paper proposes a robust adaptive nonlinear model predictive control design techniques for discrete-time nonlinear control systems. • A new set-based adaptive estimation routine is proposed to estimate the unknown parameters. • A proof of robust stability is presented for a minmax robust adaptive nonlinear model predictive control framework. • A Lipschitz-based approximation approach is used to remove the need for a minmax formulation. • A nonisothermal CSTR problem and a chemotherapy control problem are effectively solved with the design methodology. |
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• This paper proposes a robust adaptive nonlinear model predictive control design techniques for discrete-time nonlinear control systems. • A new set-based adaptive estimation routine is proposed to estimate the unknown parameters. • A proof of robust stability is presented for a minmax robust adaptive nonlinear model predictive control framework. • A Lipschitz-based approximation approach is used to remove the need for a minmax formulation. • A nonisothermal CSTR problem and a chemotherapy control problem are effectively solved with the design methodology. |
abstract_unstemmed |
• This paper proposes a robust adaptive nonlinear model predictive control design techniques for discrete-time nonlinear control systems. • A new set-based adaptive estimation routine is proposed to estimate the unknown parameters. • A proof of robust stability is presented for a minmax robust adaptive nonlinear model predictive control framework. • A Lipschitz-based approximation approach is used to remove the need for a minmax formulation. • A nonisothermal CSTR problem and a chemotherapy control problem are effectively solved with the design methodology. |
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Robust discrete-time set-based adaptive predictive control for nonlinear systems |
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code="c">2016</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">12</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• This paper proposes a robust adaptive nonlinear model predictive control design techniques for discrete-time nonlinear control systems. • A new set-based adaptive estimation routine is proposed to estimate the unknown parameters. • A proof of robust stability is presented for a minmax robust adaptive nonlinear model predictive control framework. • A Lipschitz-based approximation approach is used to remove the need for a minmax formulation. • A nonisothermal CSTR problem and a chemotherapy control problem are effectively solved with the design methodology.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Nonlinear model predictive control</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Adaptive model predictive control</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Robust model predictive control</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guay, Martin</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier Science</subfield><subfield code="a">Wu, Chen ELSEVIER</subfield><subfield code="t">A metric to gauge local distortion in metallic glasses and supercooled liquids</subfield><subfield code="d">2014transfer abstract</subfield><subfield code="d">a journal affiliated with IFAC, the International Federation of Automatic Control</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV022993630</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:39</subfield><subfield code="g">year:2016</subfield><subfield code="g">pages:111-122</subfield><subfield code="g">extent:12</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jprocont.2015.12.006</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" 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