Identification of systems with slowly sampled outputs using LPV model. (6th April 2018)

• Record Type: Journal Article
• Authors: Yan, Wengang; Zhu, Yucai; Zhu, Lingyu; Liu, Xin
• Is Part Of: Computers & chemical engineering. Volume 112(2018)
• Page Start: 316
• Page End: 330
• Publication Date: 2018-04-06
• Subjects: Identification -- Slowly sampled output -- LPV model -- OE method -- Relaxation method; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2018.02.022
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: System with slowly and irregularly sampled output is identified using a multimodel LPV (linear parameter varying) model. In the LPV model estimation, a multi-step optimization method is developed where relaxation method is used to refine the total LPV model. For LPV model structure determination, an engineering approach is proposed that combines process knowledge with the so-called final output error criteria (FOE). The results indicate LPV model considerably outperforms a linear model and a static nonlinear model. Abstract: Identification of systems with slowly sampled output is studied. A linear parameter varying (LPV) model with multi-model structure is used to solve the problem. The output error (OE) method is used to estimate model parameters. Firstly, the local models and weighting functions are estimated separately using optimization methods. Then, a relaxation iteration method is developed to refine the parameters of the total model. For LPV model structure determination, an engineering approach is proposed that combines process knowledge with the so-called final output error criteria (FOE). The method is verified using both simulation data and industrial data. In the industrial case study, the LPV models give more accurate prediction of product qualities than that of a linear dynamic model and that of a static nonlinear model; the result also indicates the necessity of using test signals in soft-sensor development.