View in EDS HTML Full Text PDF Full Text

Toward state estimation by high gain differentiators with automatic differentiation.

Saved in:
Bibliographic Details
Title: Toward state estimation by high gain differentiators with automatic differentiation.
Authors: Röbenack, Klaus1 (AUTHOR) klaus.roebenack@tu-dresden.de, Gerbet, Daniel1 (AUTHOR)
Source: Optimization Methods & Software. Apr2026, Vol. 41 Issue 2, p279-294. 16p.
Subjects: Automatic differentiation, High-order derivatives (Mathematics), Symbolic computation, Automatic control systems, Parameter estimation, Estimation theory, Observability (Control theory)
Abstract: Most applications of automatic differentiation concern the field of optimization in the broadest sense. This means that many applications only need first and second order derivatives. An exception are control engineering problems, where higher order derivatives are required. This contribution addresses a control engineering problem, namely the estimation of variables that are not measured directly. This problem can be solved with high gain observers and high gain differentiators. They are typically calculated symbolically. We show how automatic differentiation can be used for the implementation of high gain differentiators. [ABSTRACT FROM AUTHOR]
Copyright of Optimization Methods & Software is the property of Taylor & Francis Ltd 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
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 193364605
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Toward state estimation by high gain differentiators with automatic differentiation.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Röbenack%2C+Klaus%22">Röbenack, Klaus</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> klaus.roebenack@tu-dresden.de</i><br /><searchLink fieldCode="AR" term="%22Gerbet%2C+Daniel%22">Gerbet, Daniel</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Optimization+Methods+%26+Software%22">Optimization Methods & Software</searchLink>. Apr2026, Vol. 41 Issue 2, p279-294. 16p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Automatic+differentiation%22">Automatic differentiation</searchLink><br /><searchLink fieldCode="DE" term="%22High-order+derivatives+%28Mathematics%29%22">High-order derivatives (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Symbolic+computation%22">Symbolic computation</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+control+systems%22">Automatic control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Estimation+theory%22">Estimation theory</searchLink><br /><searchLink fieldCode="DE" term="%22Observability+%28Control+theory%29%22">Observability (Control theory)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Most applications of automatic differentiation concern the field of optimization in the broadest sense. This means that many applications only need first and second order derivatives. An exception are control engineering problems, where higher order derivatives are required. This contribution addresses a control engineering problem, namely the estimation of variables that are not measured directly. This problem can be solved with high gain observers and high gain differentiators. They are typically calculated symbolically. We show how automatic differentiation can be used for the implementation of high gain differentiators. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Optimization Methods & Software is the property of Taylor & Francis Ltd 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193364605
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/10556788.2024.2320737
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 279
    Subjects:
      – SubjectFull: Automatic differentiation
        Type: general
      – SubjectFull: High-order derivatives (Mathematics)
        Type: general
      – SubjectFull: Symbolic computation
        Type: general
      – SubjectFull: Automatic control systems
        Type: general
      – SubjectFull: Parameter estimation
        Type: general
      – SubjectFull: Estimation theory
        Type: general
      – SubjectFull: Observability (Control theory)
        Type: general
    Titles:
      – TitleFull: Toward state estimation by high gain differentiators with automatic differentiation.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Röbenack, Klaus
      – PersonEntity:
          Name:
            NameFull: Gerbet, Daniel
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 10556788
          Numbering:
            – Type: volume
              Value: 41
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Optimization Methods & Software
              Type: main
ResultId 1