Fast Newton-like extremum seeking with asymptotic convergence guarantees.
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| Title: | Fast Newton-like extremum seeking with asymptotic convergence guarantees. |
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| Authors: | Labar, Christophe1 (AUTHOR) christophe.labar@ic.rwth-aachen.de, Ebenbauer, Christian1 (AUTHOR) christian.ebenbauer@ic.rwth-aachen.de |
| Source: | European Journal of Control. Jul2024, Vol. 78, pN.PAG-N.PAG. 1p. |
| Subjects: | Cost functions, Dynamical systems, Prior learning |
| Abstract: | Extremum seeking denotes control approaches to steer the input of a dynamical system towards the optimizer of an associated cost function. The main strength of those schemes is to achieve their objective without any prior knowledge of the mathematical expression of the cost, nor the value of its gradient. However, those algorithms typically suffer from a slow convergence speed. In this paper, we propose a novel class of Newton-like extremum seeking systems separating the gradient and Hessian estimation from the update of the cost input towards the optimizer. The stability properties of the proposed schemes, including the ability of some of them to enforce the asymptotic convergence of the cost inputs to the optimizer, are proved. Their performances are also examined via some numerical examples. The latter will demonstrate the ability of the presented schemes to significantly reduce the convergence time compared with existing Newton-like schemes. [ABSTRACT FROM AUTHOR] |
| Copyright of European Journal of Control is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 177867274 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Fast Newton-like extremum seeking with asymptotic convergence guarantees. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Labar%2C+Christophe%22">Labar, Christophe</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> christophe.labar@ic.rwth-aachen.de</i><br /><searchLink fieldCode="AR" term="%22Ebenbauer%2C+Christian%22">Ebenbauer, Christian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> christian.ebenbauer@ic.rwth-aachen.de</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Control%22">European Journal of Control</searchLink>. Jul2024, Vol. 78, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Cost+functions%22">Cost functions</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamical+systems%22">Dynamical systems</searchLink><br /><searchLink fieldCode="DE" term="%22Prior+learning%22">Prior learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Extremum seeking denotes control approaches to steer the input of a dynamical system towards the optimizer of an associated cost function. The main strength of those schemes is to achieve their objective without any prior knowledge of the mathematical expression of the cost, nor the value of its gradient. However, those algorithms typically suffer from a slow convergence speed. In this paper, we propose a novel class of Newton-like extremum seeking systems separating the gradient and Hessian estimation from the update of the cost input towards the optimizer. The stability properties of the proposed schemes, including the ability of some of them to enforce the asymptotic convergence of the cost inputs to the optimizer, are proved. Their performances are also examined via some numerical examples. The latter will demonstrate the ability of the presented schemes to significantly reduce the convergence time compared with existing Newton-like schemes. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of European Journal of Control is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.ejcon.2024.100997 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Cost functions Type: general – SubjectFull: Dynamical systems Type: general – SubjectFull: Prior learning Type: general Titles: – TitleFull: Fast Newton-like extremum seeking with asymptotic convergence guarantees. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Labar, Christophe – PersonEntity: Name: NameFull: Ebenbauer, Christian IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 09473580 Numbering: – Type: volume Value: 78 Titles: – TitleFull: European Journal of Control Type: main |
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