<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

Parameter Identifiability for Nonlinear LPV Models

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Parameter Identifiability for Nonlinear LPV Models"}

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...

Parameter Identifiability for Nonlinear LPV Models

Online veröffentlicht: 04. Juli 2022

Seitenbereich: 255 - 269

Eingereicht: 13. Mai 2021

Akzeptiert: 02. Feb. 2022

DOI: https://doi.org/10.34768/amcs-2022-0019

Schlüsselwörter
identifiability, parameter estimation, linear parameter varying models, parity space approach, null space

© 2022 Krishnan Srinivasarengan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parameter Identifiability for Nonlinear LPV Models

Online veröffentlicht: 04. Juli 2022

Seitenbereich: 255 - 269

Eingereicht: 13. Mai 2021

Akzeptiert: 02. Feb. 2022

DOI: https://doi.org/10.34768/amcs-2022-0019

Schlüsselwörteridentifiability, parameter estimation, linear parameter varying models, parity space approach, null space

© 2022 Krishnan Srinivasarengan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
identifiability, parameter estimation, linear parameter varying models, parity space approach, null space