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基于多参数灵敏度分析与遗传优化的铁水质量无模型自适应控制

温亮 周平

温亮, 周平. 基于多参数灵敏度分析与遗传优化的铁水质量无模型自适应控制. 自动化学报, 2021, 47(11): 2600-2613 doi: 10.16383/j.aas.c180741
引用本文: 温亮, 周平. 基于多参数灵敏度分析与遗传优化的铁水质量无模型自适应控制. 自动化学报, 2021, 47(11): 2600-2613 doi: 10.16383/j.aas.c180741
Wen Liang, Zhou Ping. Model free adaptive control of molten iron quality based on multi-parameter sensitivity analysis and GA optimization. Acta Automatica Sinica, 2021, 47(11): 2600-2613 doi: 10.16383/j.aas.c180741
Citation: Wen Liang, Zhou Ping. Model free adaptive control of molten iron quality based on multi-parameter sensitivity analysis and GA optimization. Acta Automatica Sinica, 2021, 47(11): 2600-2613 doi: 10.16383/j.aas.c180741

基于多参数灵敏度分析与遗传优化的铁水质量无模型自适应控制

doi: 10.16383/j.aas.c180741
基金项目: 

国家自然科学基金 61890934

国家自然科学基金 61790572

国家自然科学基金 61473064

辽宁省"兴辽英才计划"项目 XLYC1907132

中央高校科研基金 N180802003

详细信息
    作者简介:

    温亮 东北大学硕士研究生. 2016年获得辽宁工程技术大学学士学位. 主要研究方向为无模型自适应控制. E-mail: milesinchina@outlook.com

    通讯作者:

    周平 东北大学教授. 分别于2003年, 2006年, 2013年获得东北大学学士学位、硕士学位和博士学位. 主要研究方向为工业过程运行反馈控制, 数据驱动建模与控制. 本文通信作者. E-mail: zhouping@mail.neu.edu.cn

Model Free Adaptive Control of Molten Iron Quality Based on Multi-parameter Sensitivity Analysis and GA Optimization

Funds: 

National Natural Science Foundation of China 61890934

National Natural Science Foundation of China 61790572

National Natural Science Foundation of China 61473064

Liaoning Revitalization Talents Program XLYC1907132

Fundamental Research Funds for the Central Universities N180802003

More Information
    Author Bio:

    WEN Liang Master student at Northeastern University. He received his bachelor degree from Liaoning Technical University in 2016. His main research interest is model free adaptive control

    Corresponding author: ZHOU Ping Professor at Northeastern University. He received his bachelor, master and Ph. D. degrees from Northeastern University in 2003, 2006 and 2013, respectively. His research interest covers operation feedback control of industrial process, data-driven modeling and control. Corresponding author of this paper
  • 摘要: 铁水硅含量(化学热)和铁水温度(物理热)是高炉炼铁过程最重要的铁水质量指标, 其建模与控制对于整个高炉炼铁过程的运行优化意义重大. 针对高炉炼铁过程极复杂动态特性以及铁水质量难以进行常规机理建模与控制的难题, 基于直接数据驱动控制思想, 提出一种基于多参数灵敏度分析与大规模变异遗传参数优化的高炉铁水质量无模型自适应控制方法. 首先, 基于紧格式动态线性化(Compact form dynamic linearization, CFDL)无模型自适应控制(Model free adaptive control, MFAC)技术确定铁水质量的多变量数据驱动控制器结构; 然后, 针对CFDL-MFAC众多可调参数对控制器性能影响大, 同时对众多参数整体优化非常耗时且效果不理想的问题, 基于多参数灵敏度分析(Multi-parameter sensitivity analysis, MPSA)技术, 提出基于大规模变异与精英局部搜索遗传优化的CFDL-MFAC控制器参数整定方法; 最后, 将参数整定后的CFDL-MFAC控制器应用到高炉炼铁过程多元铁水质量控制, 并与基于递推子空间辨识的数据驱动预测控制进行比较研究, 验证所提控制方法的有效性和先进性.
    Recommended by Associate Editor WU Zhou
    1)  本文责任编委 伍洲
  • 图  1  高炉工艺流程示意图

    Fig.  1  Diagram of BF ironmaking process

    图  2  CFDL-MFAC控制策略

    Fig.  2  Control strategy of CFDL-MFAC

    图  3  遗传算法最优个体收敛过程

    Fig.  3  Optimal individual convergence process of GA

    图  4  最优个体来源统计图

    Fig.  4  Optimal individual source statistics

    图  5  方波干扰测试

    Fig.  5  Square wave disturbance test

    图  6  正弦干扰测试

    Fig.  6  Sinusoidal disturbance test

    表  1  输入输出灰色关联系数

    Table  1  Grey correlation coefficients between input and output

    [Si] MIT
    压差 0.9978 0.9976
    设定喷煤量 0.9998 0.9974
    下载: 导出CSV

    表  2  CFDL-MFAC参数表

    Table  2  Parameters of CFDL-MFAC

    参数 含义 取值下限 取值上限 DS 取值 累计频率分布曲线
    $ \lambda $ 惩罚控制输入量过大变化的权重因子 0 20 0.9987 0.5
    $ \mu $ 惩罚PJM估计值过大变化的权重因子 0 20 0.9899 0.5
    $ \eta $ 伪雅可比矩阵步长因子 0 2 0.9981 0.5
    $ \rho $ 控制输入步长因子 0 1 0.6172 0.9999
    $ \alpha $ 伪雅可比矩阵取值限定参数 1 20 0.9993 1.5
    $ b_1 $ 伪雅可比矩阵取值限定参数 0 20 0.9994 0.52
    $ b_2 $ 伪雅可比矩阵取值限定参数 0 1 000 0.9990 0.8
    $ \varphi _{11} $ 伪雅可比矩阵初值 $ - $20 20 0.4975 0.5143
    $ \varphi _{12} $ 伪雅可比矩阵初值 $ - $20 20 0.4840 $ - $1.1435
    $ \varphi _{21} $ 伪雅可比矩阵初值 $ - $20 20 0.0795 1.1436
    $ \varphi _{22} $ 伪雅可比矩阵初值 $ - $20 20 0.7548 0.5144
    下载: 导出CSV

    表  3  GA参数设定

    Table  3  Set value of GA parameters

    参数 参数含义 取值
    $ g_e $ 最大遗传代数 300
    $ r $ 种群规模 50
    $ g_b $ 染色体变异概率 0.0125
    $ g_s $ 大规模变异概率 0.25
    $ g_j $ 染色体交叉概率 0.7
    $ m_b $ 变异算子精度 20
    $ T $ Metropolis准则温度参数 1
    $ \alpha _T $ Metropolis准则温度衰减系数 0.5
    下载: 导出CSV

    表  4  控制器性能对比

    Table  4  Control performance comparision

    模型 CFDL-MFAC RSMI-DPC
    测试样本数 250 250
    平均控制量更新时间(s) 0.000019 0.0513
    方波扰动下[Si]含量RMSE (%) 0.0364 0.0792
    方波扰动下MIT RMSE (℃) 6.3768 9.6553
    正弦扰动下[Si]含量RMSE (%) 0.0229 0.0524
    正弦扰动下MIT RMSE (℃) 3.0290 5.3546
    下载: 导出CSV
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  • 收稿日期:  2018-11-07
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