Identification of Multimodel LPV Models with Asymmetric Gaussian Weighting Function

This paper is concerned with the identification of linear parameter varying (LPV) systems by utilizing a multimodel structure. To improve the approximation capability of the LPV model, asymmetric Gaussian weighting functions are introduced and compared with commonly used symmetric Gaussian functions. By this mean, locations of operating points can be selected freely. It has been demonstrated through simulations with a high purity distillation column that the identified models provide more satisfactory approximation. Moreover, an experiment is performed on real HVAC (heating, ventilation, and air-conditioning) to further validate the effectiveness of the proposed approach

Model-plant Mismatch Detection of Nonlinear Processes Based on Multi-model LPV Model

The performance of model-based controller depends on the quality of the identified model. Accurate detection of the channel with model-plant mismatch can avoid re-identification of the entire multivariable system, thereby reducing the disturbance to normal production caused by identification test. A model-plant mismatch detection methodology for nonlinear systems based on LPV (Linear Parameter Varying) model was proposed in this work. The detection was performed only when the control performace becomes worse. Firstly, the LPV model based on multi-model interpolation was adopted to represent the nonlinear process. Then partial correlation coefficients between the model residuals and the inputs of the models at each of the operation points were analyzed to diagnose the model-plant mismatch of the local models. Finally, the LPV model was re-identified by updating the local mismatch models and re-estimating the model weighing parameters. The experimental results show that the partial correlation coefficient of the mismatch model is obviously larger than that of the exact model, which can point out the channel with model-plant mismatch correctly.The proposed method is suitable for the nonlinear processes which have relative steady states in their operating trajectorys

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. (C) 2012 Elsevier Ltd. All rights reserved.National Natural Science Foundation of China [61174161]; Key Research Project of Fujian Province of China [2009H0044]; Fundamental Research Fund for the Central Universities of Xiamen University of China [201212G005]; National Science Foundation of China [60934007]; 973 Program of China [2012CB720500

Some study on the identification of multi-model LPV models with two scheduling variables

Conference Name:Universite Libre de Bruxelles. Conference Address: Bruxelles, Belgium. Time:July 11, 2012 - July 13, 2012.IFAC Technical Committee on Modeling,; Identification and Signal Processing; IFAC Technical Committee on Adaptive and; Learning Systems; IFAC Technical Committee on Discrete; Events and Hybrid SystemsThis work studies the identification of LPV (linear parameter varying) models with two scheduling variables in order to model complex industrial processes. The LPV model is parameterized as blended linear models, which is also called multi-model approach. 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 also 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. 漏 2012 IFAC

Implementation of a Classification-Based Prediction Model for Plant mRNA Poly(A) Sites

The poly(A) site of a messenger RNA (mRNA) defines the end of a transcript during eukaryotic gene expression. Finding poly(A) sites in genome sequences can help to annotate the ends of genes and predict alternative polyadenylation. However, it is challenging to predict plant poly(A) sites using computational methods because of the weak signals that determine the poly(A) sites. Here we describe a classification based plant poly(A) site recognition model. First, several feature representation methods like factorial moments, M encoding, and weight of signal patterns are adopted to describe the makeup of nucleotide sequences of poly(A) signals. Then, a training model using different classification algorithms like Bayesian Network is built as a testing model to predict plant mRNA poly(A) sites. Comparing to previous plant poly(A) sites prediction software PASS that we developed, the recognition model introduced here has better performance, flexibility and expansibility

Identification and predictive control for a circulation fluidized bed boiler

Key Research Project of Fujian Province of China [2009H0044]; National Natural Science Foundation of China [61174161]; National Important Project [2010ZX04001-162]; Key Research Project of Xiamen City of China [3502Z20123014]; Fundamental Research Funds for the Central Universities in China (Xiamen University) [201212G005]; Fundamental Research Fund for the university student Creative and Entrepreneurship training program in China (Xiamen University) [XDDC201210384063]; National Science Foundation of China [60934007]; 973 Program of China [201203720500]This paper introduces the design and presents the research findings of the identification and control application for an industrial Circulation Fluidized Bed (CFB) boiler. Linear Parameter Varying (LPV) model is used in the model identification where steam flow is selected as the operation-point (scheduling) variable. Three kinds of weighting functions, namely linear, cubic splines and Gaussian functions are compared. LPV model based Model Predictive Control (MPC) is also simulated. Test results show that LPV model is more accurate than linear model, and LPV MPC yields a better control effect than linear MPC. (C) 2013 Elsevier B.V. All rights reserved