Title: Identification of multi-model LPV model with two scheduling variables using transition test

Authors: Jiangyin Huang; Jing Zhao

Addresses: Department of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen 361024, Fujian, China ' Department of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen 361024, Fujian, China

Abstract: This paper presents the research findings of identification method for LPV models with two scheduling variables using transition tests. The LPV model is parameterised as blended linear models, which is also called a multi-model structure. The identification method proposed in this paper can be used in batch process identification. The usefulness of the method is verified by modelling a high purity distillation column. The outputs of the LPV models are compared and analysed with three kinds of weighting functions namely: linear, polynomial and Gaussian functions. The case study shows that the multi-model LPV models can yield a better model accuracy with respect to simulation outputs and step response fittings than linear models.

Keywords: LPV model; multi-model; transition test; high purity distillation column.

DOI: 10.1504/IJMIC.2018.089621

International Journal of Modelling, Identification and Control, 2018 Vol.29 No.1, pp.31 - 43

Received: 13 Apr 2016
Accepted: 10 Jan 2017
Published online: 02 Feb 2018 *

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