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多变量逆解耦自抗扰控制及其在精馏塔过程中的应用

程赟 陈增强 孙明玮 孙青林

程赟, 陈增强, 孙明玮, 孙青林. 多变量逆解耦自抗扰控制及其在精馏塔过程中的应用. 自动化学报, 2017, 43(6): 1080-1088. doi: 10.16383/j.aas.2017.c170137
引用本文: 程赟, 陈增强, 孙明玮, 孙青林. 多变量逆解耦自抗扰控制及其在精馏塔过程中的应用. 自动化学报, 2017, 43(6): 1080-1088. doi: 10.16383/j.aas.2017.c170137
CHENG Yun, CHEN Zeng-Qiang, SUN Ming-Wei, SUN Qing-Lin. Multivariable Inverted Decoupling Active Disturbance Rejection Control and Its Application to a Distillation Column Process. ACTA AUTOMATICA SINICA, 2017, 43(6): 1080-1088. doi: 10.16383/j.aas.2017.c170137
Citation: CHENG Yun, CHEN Zeng-Qiang, SUN Ming-Wei, SUN Qing-Lin. Multivariable Inverted Decoupling Active Disturbance Rejection Control and Its Application to a Distillation Column Process. ACTA AUTOMATICA SINICA, 2017, 43(6): 1080-1088. doi: 10.16383/j.aas.2017.c170137

多变量逆解耦自抗扰控制及其在精馏塔过程中的应用

doi: 10.16383/j.aas.2017.c170137
基金项目: 

天津市自然科学基金 14JCYBJC18700

国家自然科学基金 61573197

国家自然科学基金 61573199

详细信息
    作者简介:

    程赟 南开大学博士研究生.主要研究方向为自抗扰控制.E-mail: 1120160118@mail.nankai.edu.cn

    孙明玮 南开大学教授.主要研究方向为飞行器制导与控制, 自抗扰控制.E-mail: smw_sunmingwei@163.com

    孙青林 南开大学教授.主要研究方向为自适应控制, 柔性航空器建模与控制. E-mail: sunql@nankai.edu.cn

    通讯作者:

    陈增强 南开大学教授.主要研究方向为自抗扰控制, 预测控制. E-mail: chenzq@nankai.edu.cn

Multivariable Inverted Decoupling Active Disturbance Rejection Control and Its Application to a Distillation Column Process

Funds: 

the Natural Science Foundation of Tianjin 14JCYBJC18700

National Natural Science Foundation of China 61573197

National Natural Science Foundation of China 61573199

More Information
    Author Bio:

    Ph. D. candidate at Nankai University. His main research interest is active disturbance rejection control

    Professor at Nankai University. His research interest covers guidance and control for flight aircraft and active disturbance rejection control

    Professor at Nankai University. His research interest covers self-adaptive control, modeling and controlling of flexible spacecraft

    Corresponding author: CHEN Zeng-Qiang Professor at Nankai University. His research interest covers active disturbance rejection control and predictive control. Corresponding author of this paper
  • 摘要: 化工生产是一类典型的多变量过程对象,该类对象具有时变性、耦合性、时滞性等特点,传统的单变量控制方法很难在此类系统中取得良好的控制效果.针对时变性,本文在假设对象模型未知的前提下,利用阶跃响应数据,研究了基于最小二乘的一阶、二阶时滞系统辨识方法.针对多变量系统存在耦合性的特点,采用逆解耦方法实现对象的解耦.再对解耦后的时滞子系统设计了自抗扰控制器.仿真实验中,以精馏塔的Wood-Berry模型和Ogunnaike-Ray模型为例,验证了算法的有效性.
    1)  本文责任编委 王伟
  • 图  1  TITO系统逆解耦器结构图

    Fig.  1  Inverted decoupling structure of TITO system

    图  2  三种方法跟踪轨迹以及抗扰性能

    Fig.  2  Tracking and disturbance rejection performance of three methods

    图  3  采用NSR=1 %下辨识数据控制效果

    Fig.  3  Control efiect by using identiflcation data of NSR=1%

    图  4  采用NSR=10 %下辨识数据控制效果

    Fig.  4  Control efiect by using identiflcation data of NSR=10 %

    图  5  三种方法跟踪轨迹以及抗扰性能

    Fig.  5  Tracking and disturbance rejection performance of three methods

    图  6  采用NSR=1 %下辨识数据控制效果

    Fig.  6  Control efiect by using identiflcation data of NSR=1 %

    图  7  采用NSR=10 %下辨识数据控制效果

    Fig.  7  Control efiect by using identiflcation data of NSR=10 %

    表  1  Wood-Berry模型辨识结果的均方误差

    Table  1  Mean square error of the Wood-Berry model

    G11G12G21G22
    NSR=1%1.93×10-46.37×10-52.98×10-54.69×10-5
    NSR=10%4.88×10-56.32×10-46.62×10-42.10×10-3
    下载: 导出CSV

    表  2  三种算法IAE指标对比

    Table  2  Comparison of IAE for three methods

    IAE1IAE2Sum
    3-ADRC6.0115.1521.16
    ID-ADRC5.2521.0926.34
    ID-TDADRC2.598.7711.36
    下载: 导出CSV

    表  3  采用辨识数据控制下的IAE指标

    Table  3  IAE by using identiflcation data

    IAE1IAE2Sum
    NSR=1%2.628.7811.40
    NSR=10%2.738.7711.50
    下载: 导出CSV

    表  4  三种算法IAE指标对比

    Table  4  Comparison of IAE for three methods

    G11G12G13G21G22G23G31G32G33
    NSR=1%1.62×10-75.19×10-77.08×10-123.63×10-61.94×10-61.07×10-105.60×10-44.69×10-51.38×10-6
    NSR=10%2.36×10-81.35×10-61.95×10-101.61×10-63.45×10-63.12×10-102.70×10-32.00×10-32.38×10-4
    下载: 导出CSV

    表  5  三种算法IAE指标对比

    Table  5  Comparison of IAE for three methods

    IAE1IAE2IAE3Sum
    3-ADRC15.2223.4119.2357.86
    ID-ADRC17.7510.6535.1663.56
    ID-TDADRC4.595.418.8618.86
    下载: 导出CSV

    表  6  采用辨识数据控制下的IAE指标

    Table  6  IAE by using identiflcation data

    IAE1IAE2IAE3Sum
    NSR=1%4.615.5017.8527.96
    NSR=10%4.635.5538.1548.33
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-03-13
  • 录用日期:  2017-05-26
  • 刊出日期:  2017-06-20

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