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Identification of LPV Models with Two Scheduling Variables Using Transition Test

This paper presents the research findings of identification method for LPV models with two scheduling variables using transition test. The LPV model is parameterized as blended linear models, which is also called as multi-model structure. Linear weighting functions are used as the local model weights and the Gauss-Newton method is used to optimize the nonlinear LPV model parameters. Usefulness of the method is verified by modeling a high purity distillation column, the case study shows that the multi-model LPV models can yield a better model accuracy with respect to simulation outputs. The identification method proposed in this paper can be used in batch process identification.