- Tytuł:
- Parameter identifiability for nonlinear LPV models
- Autorzy:
-
Srinivasarengan, Krishnan
Ragot, José
Aubrun, Christophe
Maquin, Didier - Powiązania:
- https://bibliotekanauki.pl/articles/2134053.pdf
- Data publikacji:
- 2022
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
parameter identifiability
parameter estimation
linear parameter varying model
parity space approach
null space
identyfikacja parametrów
szacowanie parametrów
spacja zerowa - Opis:
- 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2022, 32, 2; 255--269
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł