Parameter identifiability for nonlinear LPV models

• Autorzy: Srinivasarengan Krishnan , Ragot José , Aubrun Christophe , Maquin Didier

Identyfikatory

DOI

10.34768/amcs-2022-0019

• Języki publikacji: EN

Abstrakty

EN

Linear parameter varying (LPV) models are being increasingly used as a bridge between linear and nonlinear models. From a mathematical point of view, a large class of nonlinear models can be rewritten in LPV or quasi-LPV forms easing their analysis. From a practical point of view, that kind of model can be used for introducing varying model parameters representing, for example, nonconstant characteristics of a component or an equipment degradation. This approach is frequently employed in several model-based system maintenance methods. The identifiability of these parameters is then a key issue for estimating their values based on which a decision can be made. However, the problem of identifiability of these models is still at a nascent stage. In this paper, we propose an approach to verify the identifiability of unknown parameters for LPV or quasi-LPV state-space models. It makes use of a parity-space like formulation to eliminate the states of the model. The resulting input-output-parameter equation is analyzed to verify the identifiability of the original model or a subset of unknown parameters. This approach provides a framework for both continuous-time and discrete-time models and is illustrated through various examples.

Słowa kluczowe

EN

parameter identifiability; parameter estimation; linear parameter varying model; parity space approach; null space

PL

identyfikacja parametrów; szacowanie parametrów; spacja zerowa

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2022
• Tom: Vol. 32, no. 2
• Strony: 255--269
• Opis fizyczny: Bibliogr. 44 poz.

Twórcy

autor

Srinivasarengan Krishnan

• CRAN, University of Lorraine/CNRS, 54000, Nancy, France

autor

Ragot José

• CRAN, University of Lorraine/CNRS, 54000, Nancy, France

autor

Aubrun Christophe

• CRAN, University of Lorraine/CNRS, 54000, Nancy, France

autor

Maquin Didier

• CRAN, University of Lorraine/CNRS, 54000, Nancy, France

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