Parameter identifiability for nonlinear LPV models

International audienceLinear 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 easingtheir analysis. From a practical point of view, that kind of model can be used for introducing varying model parametersrepresenting, for example, nonconstant characteristics of a component or an equipment degradation. This approach isfrequently employed in several model-based system maintenance methods. The identifiability of these parameters is thena key issue for estimating their values based on which a decision can be made. However, the problem of identifiabilityof these models is still at a nascent stage. In this paper, we propose an approach to verify the identifiability of unknownparameters for LPV or quasi-LPV state-space models. It makes use of a parity-space like formulation to eliminate the statesof the model. The resulting input-output-parameter equation is analyzed to verify the identifiability of the original model ora subset of unknown parameters. This approach provides a framework for both continuous-time and discrete-time modelsand is illustrated through various examples