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
Gespeichert in:
| 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.] |
|---|---|
| Übergeordnetes Werk: |
volume:39 ; year:2016 ; pages:111-122 ; extent:12 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.jprocont.2015.12.006 |
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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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• 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. |
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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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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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