Identification of multi-model LPV models with two scheduling variables

In order to model complex industrial processes, this work studies the identification of linear parameter varying (LPV) models with two scheduling variables. The LPV model is parameterized as blended linear models, which is also called multi-model structure. Several weighting functions, linear, polynomial and Gaussian functions, are used and compared. The usefulness of the method is tested using a high purity distillation column model in a case study. The case study shows that a good fit of identification data is not enough to verify model quality and can even be misleading in nonlinear process identification; other measures related to process knowledge should be used in model validation. The case study also shows that commonly used LPV model based on parameter interpolation can fail for the high purity distillation column. Finally, several pitfalls in nonlinear process identification are pointed out.

► Identification of LPV models with two scheduling variables is studied.
► The LPV model is parameterized in a multi-model structure.
► The method is tested using a high purity distillation column model.
► Commonly used LPV model can fail for the high purity distillation column.
► Several pitfalls in nonlinear process identification are pointed out.