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Front. Inform. Technol. Electron. Eng.  2011, Vol. 12 Issue (1): 25-35    DOI: 10.1631/jzus.C0910779
    
Wiener model identification and nonlinear model predictive control of a pH neutralization process based on Laguerre filters and least squares support vector machines
Qing-chao Wang1, Jian-zhong Zhang*,1,2
1 School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China 2 SINOPEC Safety Engineering Institute, Qingdao 266071, China
Wiener model identification and nonlinear model predictive control of a pH neutralization process based on Laguerre filters and least squares support vector machines
Qing-chao Wang1, Jian-zhong Zhang*,1,2
1 School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China 2 SINOPEC Safety Engineering Institute, Qingdao 266071, China
 全文: PDF(229 KB)  
摘要： This paper deals with Wiener model based predictive control of a pH neutralization process. The dynamic linear block of the Wiener model is parameterized using Laguerre filters while the nonlinear block is constructed using least squares support vector machines (LSSVM). Input-output data from the first principle model of the pH neutralization process are used for the Wiener model identification. Simulation results show that the proposed Wiener model has higher prediction accuracy than Laguerre-support vector regression (SVR) Wiener models, Laguerre-polynomial Wiener models, and linear Laguerre models. The identified Wiener model is used here for nonlinear model predictive control (NMPC) of the pH neutralization process. The set-point tracking performance of the proposed NMPC is compared with those of the Laguerre-SVR Wiener model based NMPC, Laguerre-polynomial Wiener model based NMPC, and linear model predictive control (LMPC). Validation results show that the proposed NMPC outperforms the other three controllers.
关键词： Wiener model;  Nonlinear model predictive control (NMPC);  pH neutralization process;  Laguerre filters;  Least squares support vector machines (LSSVM)    
Abstract: This paper deals with Wiener model based predictive control of a pH neutralization process. The dynamic linear block of the Wiener model is parameterized using Laguerre filters while the nonlinear block is constructed using least squares support vector machines (LSSVM). Input-output data from the first principle model of the pH neutralization process are used for the Wiener model identification. Simulation results show that the proposed Wiener model has higher prediction accuracy than Laguerre-support vector regression (SVR) Wiener models, Laguerre-polynomial Wiener models, and linear Laguerre models. The identified Wiener model is used here for nonlinear model predictive control (NMPC) of the pH neutralization process. The set-point tracking performance of the proposed NMPC is compared with those of the Laguerre-SVR Wiener model based NMPC, Laguerre-polynomial Wiener model based NMPC, and linear model predictive control (LMPC). Validation results show that the proposed NMPC outperforms the other three controllers.
Key words: Wiener model    Nonlinear model predictive control (NMPC)    pH neutralization process    Laguerre filters    Least squares support vector machines (LSSVM)
收稿日期: 2009-12-21 出版日期: 2010-01-10
CLC:  TP273  
基金资助: Project (No. 60574022) supported by the National Natural Science Foundation of China
通讯作者: Jian-zhong Zhang     E-mail: zjzhit1984@163.com
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Qing-chao Wang, Jian-zhong Zhang. Wiener model identification and nonlinear model predictive control of a pH neutralization process based on Laguerre filters and least squares support vector machines. Front. Inform. Technol. Electron. Eng., 2011, 12(1): 25-35.

链接本文:

http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C0910779    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2011/V12/I1/25

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