Parametrizing mechanical systems using matrix fraction descriptions: with application to spatio-temporal identification

R. de Rozario, R.J. Voorhoeve, T.A.E. Oomen

Research output: Contribution to conferencePosterAcademic

Abstract

Identifying accurate Linear Parameter Varying models of flexible mechanical systems is crucial for the inference and control of unmeasured performance variables in high-performance mechatronic systems. A succesfull grey-box strategy to obtain accurate models through a local approach is to directly identify canonical mechanical systems, whose parameters can subsequently be interpolated in a physically motivated manner. Estimating these generally nonlinear-in-the parameter modal models from Frequency Response Function data through gradient-based techniques suffers from the potential of converging to sub-optimal estimates. In this research proposes a novel Matrix-Fraction Description parametrisation that is shown to be equivalent to the relevant class of modal mechanical systems. This parametrisation enables the formulation of the Sanathanan-and-Koerner and Instrumental Variables estimators which are known to attain the global optimum upon convergence.
The proposed parametrisation is successfully employed to directly estimate a large set of local models of various position-dependent motion systems, thereby confirming that proposed the identification method is well-suited for practical applications.
Translated title of the contributionHet parametriseren van mechanische systemen met matrix fractie beschrijvingen: met toepassing op spatieel-temporale identificatie
LanguageEnglish
StatePublished - 27 Sep 2017
EventEuropean Research Network System Identification Workshop - Domaine Lyon Saint Joseph, Lyon, France
Duration: 24 Sep 201727 Sep 2017
Conference number: 26
https://ernsi2017.sciencesconf.org/

Conference

ConferenceEuropean Research Network System Identification Workshop
Abbreviated titleERNSI
CountryFrance
CityLyon
Period24/09/1727/09/17
Internet address

Keywords

    Cite this

    de Rozario, R., Voorhoeve, R. J., & Oomen, T. A. E. (2017). Parametrizing mechanical systems using matrix fraction descriptions: with application to spatio-temporal identification. Poster session presented at European Research Network System Identification Workshop, Lyon, France.
    de Rozario, R. ; Voorhoeve, R.J. ; Oomen, T.A.E./ Parametrizing mechanical systems using matrix fraction descriptions : with application to spatio-temporal identification. Poster session presented at European Research Network System Identification Workshop, Lyon, France.
    @conference{3fa6512ca5ee43a182bf38a78f6a2859,
    title = "Parametrizing mechanical systems using matrix fraction descriptions: with application to spatio-temporal identification",
    abstract = "Identifying accurate Linear Parameter Varying models of flexible mechanical systems is crucial for the inference and control of unmeasured performance variables in high-performance mechatronic systems. A succesfull grey-box strategy to obtain accurate models through a local approach is to directly identify canonical mechanical systems, whose parameters can subsequently be interpolated in a physically motivated manner. Estimating these generally nonlinear-in-the parameter modal models from Frequency Response Function data through gradient-based techniques suffers from the potential of converging to sub-optimal estimates. In this research proposes a novel Matrix-Fraction Description parametrisation that is shown to be equivalent to the relevant class of modal mechanical systems. This parametrisation enables the formulation of the Sanathanan-and-Koerner and Instrumental Variables estimators which are known to attain the global optimum upon convergence.The proposed parametrisation is successfully employed to directly estimate a large set of local models of various position-dependent motion systems, thereby confirming that proposed the identification method is well-suited for practical applications.",
    keywords = "identification, Mechanical systems",
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    year = "2017",
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    day = "27",
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    note = "null ; Conference date: 24-09-2017 Through 27-09-2017",
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    Parametrizing mechanical systems using matrix fraction descriptions : with application to spatio-temporal identification. / de Rozario, R.; Voorhoeve, R.J.; Oomen, T.A.E.

    2017. Poster session presented at European Research Network System Identification Workshop, Lyon, France.

    Research output: Contribution to conferencePosterAcademic

    TY - CONF

    T1 - Parametrizing mechanical systems using matrix fraction descriptions

    T2 - with application to spatio-temporal identification

    AU - de Rozario,R.

    AU - Voorhoeve,R.J.

    AU - Oomen,T.A.E.

    PY - 2017/9/27

    Y1 - 2017/9/27

    N2 - Identifying accurate Linear Parameter Varying models of flexible mechanical systems is crucial for the inference and control of unmeasured performance variables in high-performance mechatronic systems. A succesfull grey-box strategy to obtain accurate models through a local approach is to directly identify canonical mechanical systems, whose parameters can subsequently be interpolated in a physically motivated manner. Estimating these generally nonlinear-in-the parameter modal models from Frequency Response Function data through gradient-based techniques suffers from the potential of converging to sub-optimal estimates. In this research proposes a novel Matrix-Fraction Description parametrisation that is shown to be equivalent to the relevant class of modal mechanical systems. This parametrisation enables the formulation of the Sanathanan-and-Koerner and Instrumental Variables estimators which are known to attain the global optimum upon convergence.The proposed parametrisation is successfully employed to directly estimate a large set of local models of various position-dependent motion systems, thereby confirming that proposed the identification method is well-suited for practical applications.

    AB - Identifying accurate Linear Parameter Varying models of flexible mechanical systems is crucial for the inference and control of unmeasured performance variables in high-performance mechatronic systems. A succesfull grey-box strategy to obtain accurate models through a local approach is to directly identify canonical mechanical systems, whose parameters can subsequently be interpolated in a physically motivated manner. Estimating these generally nonlinear-in-the parameter modal models from Frequency Response Function data through gradient-based techniques suffers from the potential of converging to sub-optimal estimates. In this research proposes a novel Matrix-Fraction Description parametrisation that is shown to be equivalent to the relevant class of modal mechanical systems. This parametrisation enables the formulation of the Sanathanan-and-Koerner and Instrumental Variables estimators which are known to attain the global optimum upon convergence.The proposed parametrisation is successfully employed to directly estimate a large set of local models of various position-dependent motion systems, thereby confirming that proposed the identification method is well-suited for practical applications.

    KW - identification

    KW - Mechanical systems

    UR - http://www.dct.tue.nl/toomen/files/RozarioVooOom2017poster.pdf

    M3 - Poster

    ER -

    de Rozario R, Voorhoeve RJ, Oomen TAE. Parametrizing mechanical systems using matrix fraction descriptions: with application to spatio-temporal identification. 2017. Poster session presented at European Research Network System Identification Workshop, Lyon, France.