Identification of multiple linear models for nonlinear processes
Journal
Journal of the Chinese Institute of Chemical Engineers
Journal Volume
31
Journal Issue
3
Pages
283-293
Date Issued
2000
Author(s)
Abstract
This work presents a nonlinear dynamic model based on several local linear models under different operating conditions. Response of the global nonlinear dynamic model is also derived by weighting the sum of all local linear model outputs. In addition, the fuzzy set theory is applied to account for the weighting factors for the local models. Also presented herein are two novel means of estimating the multiple linear models' output: The parameter interpolation method and the output difference interpolation method. According to our results, these two methods are identical in terms of interpolating the difference of state vector, outputs, and inputs. Some major identification methods, e.g., linearization of the first-principle model, identification of linear local models, and least squares algorithm, are proposed. Several typical nonlinear processes are used to demonstrate the effectiveness of the multiple linear model identification.This work presents a nonlinear dynamic model based on several local linear models under different operating conditions. Response of the global nonlinear dynamic model is also derived by weighting the sum of all local linear model outputs. In addition, the fuzzy set theory is applied to account for the weighting factors for the local models. Also presented herein are two novel means of estimating the multiple linear models' output: the parameter interpolation method and the output difference interpolation method. According to our results, these two methods are identical in terms of interpolating the difference of state vector, outputs, and inputs. Some major identification methods, e.g., linearization of the first-principle model, identification of linear local models, and least squares algorithm, are proposed. Several typical nonlinear processes are used to demonstrate the effectiveness of the multiple linear model identification.
Subjects
Fuzzy set
Identification
Linear model
Nonlinear process
Type
journal article