Srinivasarengan, Krishnan ; Ragot, José ; Aubrun, Christophe ; Maquin, Didier
Współtwórca:Witczak, Marcin - ed. ; Stetter, Ralf - ed.
Tytuł:Parameter identifiability for nonlinear LPV models
Podtytuł: Tytuł publikacji grupowej: Temat i słowa kluczowe:identifiability ; parameter estimation ; linear parameter varying models ; parity space approach ; null space
Abstract: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.
Wydawca:Zielona Góra: Uniwersytet Zielonogórski
Data wydania: Typ zasobu: DOI: Strony: Źródło:AMCS, volume 32, number 2 (2022) ; kliknij tutaj, żeby przejść
Jezyk: Licencja CC BY 4.0: Prawa do dysponowania publikacją: