An approach to parameter identifiability for a class of nonlinear models represented in LPV form

In several model-based system maintenance problems, parameters are used to represent unknown characteristics of a component, equipment degradation, etc. This allows for modelling constant, slow-varying terms. The identifiability of these parameters is an important condition to estimate them. Linear Parameter Varying (LPV) models are being increasingly used in the industries as a bridge between linear and nonlinear models. Techniques exist that can rewrite some nonlinear models in LPV form. However, the problem of identifiability of these models is still at a nascent stage. In this paper, we propose an approach to verify identifiability of unknown parameters for 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 analysed 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 we illustrate it using examples