Het parametriseren van mechanische systemen met matrix fractie beschrijvingen: met toepassing op spatieel-temporale identificatie

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

Onderzoeksoutput: Bijdrage aan congresPosterAcademic

Uittreksel

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.
Vertaalde titel van de bijdrageHet parametriseren van mechanische systemen met matrix fractie beschrijvingen: met toepassing op spatieel-temporale identificatie
TaalEngels
StatusGepubliceerd - 27 sep 2017
EvenementEuropean Research Network System Identification Workshop - Domaine Lyon Saint Joseph, Lyon, Frankrijk
Duur: 24 sep 201727 sep 2017
Congresnummer: 26
https://ernsi2017.sciencesconf.org/

Congres

CongresEuropean Research Network System Identification Workshop
Verkorte titelERNSI
LandFrankrijk
StadLyon
Periode24/09/1727/09/17
Internet adres

Trefwoorden

  • identification
  • Mechanical systems

Citeer dit

de Rozario, R., Voorhoeve, R. J., & Oomen, T. A. E. (2017). Parametrizing mechanical systems using matrix fraction descriptions: with application to spatio-temporal identification. Postersessie gepresenteerd op European Research Network System Identification Workshop, Lyon, Frankrijk.
de Rozario, R. ; Voorhoeve, R.J. ; Oomen, T.A.E./ Parametrizing mechanical systems using matrix fraction descriptions : with application to spatio-temporal identification. Postersessie gepresenteerd op European Research Network System Identification Workshop, Lyon, Frankrijk.
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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.",
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de Rozario, R, Voorhoeve, RJ & Oomen, TAE 2017, 'Parametrizing mechanical systems using matrix fraction descriptions: with application to spatio-temporal identification' European Research Network System Identification Workshop, Lyon, Frankrijk, 24/09/17 - 27/09/17, .

Parametrizing mechanical systems using matrix fraction descriptions : with application to spatio-temporal identification. / de Rozario, R.; Voorhoeve, R.J.; Oomen, T.A.E.

2017. Postersessie gepresenteerd op European Research Network System Identification Workshop, Lyon, Frankrijk.

Onderzoeksoutput: Bijdrage aan congresPosterAcademic

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

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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.

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de Rozario R, Voorhoeve RJ, Oomen TAE. Parametrizing mechanical systems using matrix fraction descriptions: with application to spatio-temporal identification. 2017. Postersessie gepresenteerd op European Research Network System Identification Workshop, Lyon, Frankrijk.