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作者:

Li, Xiaoli (Li, Xiaoli.) (学者:李晓理) | Dong, Jiawei (Dong, Jiawei.) | Wang, Kang (Wang, Kang.)

收录:

SCIE

摘要:

In wet flue gas desulfurization (WFGD) process, the pH value of the absorption tower slurry is a crucial factor to the efficiency of desulfurization system. Aiming at the nonlinearity and large lag of the pH change in WFGD process, a predictive control strategy based on Hammerstein-Wiener inverse model compensation is proposed. During the calculation of optimal control, an anti-model of Wiener nonlinearity unit is adopted to transform the output setting values and sampling values. Similarly in the control process, the controller output is applied to the actual controlled object after inverse transformation of the static nonlinear Hammerstein model. Through the above two inverse transformations, the controller output is identical with the input of linear link in the closed-loop system. In this article, the inverse model compensation method is utilized to transform nonlinear process control into linear system control, avoiding the large computation of nonlinear model optimization. Finally, the feasibility and effectiveness of the proposed scheme are verified by simulation.

关键词:

Hammerstein-Wiener model inverse model compensation nonlinear model predictive control wet flue gas desulfurization

作者机构:

  • [ 1 ] [Li, Xiaoli]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Dong, Jiawei]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Wang, Kang]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Li, Xiaoli]Beijing Univ Technol, Beijing Key Lab Computat Intelligence & Intellige, Beijing, Peoples R China
  • [ 5 ] [Li, Xiaoli]Beijing Univ Technol, Engn Res Ctr Digital Community, Beijing, Peoples R China

通讯作者信息:

  • 李晓理

    [Li, Xiaoli]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China

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来源 :

OPTIMAL CONTROL APPLICATIONS & METHODS

ISSN: 0143-2087

年份: 2021

1 . 8 0 0

JCR@2022

ESI学科: ENGINEERING;

ESI高被引阀值:9

被引次数:

WoS核心集被引频次: 3

SCOPUS被引频次: 3

ESI高被引论文在榜: 0 展开所有

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