1. bookVolume 32 (2022): Edition 2 (June 2022)
    Towards Self-Healing Systems through Diagnostics, Fault-Tolerance and Design (Special section, pp. 171-269), Marcin Witczak and Ralf Stetter (Eds.)
Détails du magazine
Première parution
05 Apr 2007
4 fois par an
access type Accès libre

Parameter Identifiability for Nonlinear LPV Models

Publié en ligne: 04 Jul 2022
Volume & Edition: Volume 32 (2022) - Edition 2 (June 2022)<br/>Towards Self-Healing Systems through Diagnostics, Fault-Tolerance and Design (Special section, pp. 171-269), Marcin Witczak and Ralf Stetter (Eds.)
Pages: 255 - 269
Reçu: 13 May 2021
Accepté: 02 Feb 2022
Détails du magazine
Première parution
05 Apr 2007
4 fois par an

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.


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