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无线网络环境下数据驱动混合选别浓密过程双率控制方法

吴倩 范家璐 姜艺 柴天佑

吴倩, 范家璐, 姜艺, 柴天佑. 无线网络环境下数据驱动混合选别浓密过程双率控制方法. 自动化学报, 2019, 45(6): 1122-1135. doi: 10.16383/j.aas.c180202
引用本文: 吴倩, 范家璐, 姜艺, 柴天佑. 无线网络环境下数据驱动混合选别浓密过程双率控制方法. 自动化学报, 2019, 45(6): 1122-1135. doi: 10.16383/j.aas.c180202
WU Qian, FAN Jia-Lu, JIANG Yi, CHAI Tian-You. Data-driven Dual-rate Control for Mixed Separation Thickening Process in a Wireless Network Environment. ACTA AUTOMATICA SINICA, 2019, 45(6): 1122-1135. doi: 10.16383/j.aas.c180202
Citation: WU Qian, FAN Jia-Lu, JIANG Yi, CHAI Tian-You. Data-driven Dual-rate Control for Mixed Separation Thickening Process in a Wireless Network Environment. ACTA AUTOMATICA SINICA, 2019, 45(6): 1122-1135. doi: 10.16383/j.aas.c180202

无线网络环境下数据驱动混合选别浓密过程双率控制方法

doi: 10.16383/j.aas.c180202
基金项目: 

国家自然科学基金 61333012

中央高校基本科研专项资金 N160804001

国家自然科学基金 61333012

国家自然科学基金 61333012

详细信息
    作者简介:

    吴倩  东北大学流程工业综合自动化国家重点实验室硕士研究生.主要研究方向为工业过程运行控制, 网络控制, 强化学习.E-mail:wuqian_neu@163.com

    姜艺  东北大学流程工业综合自动化国家重点实验室博士研究生.2016年获得东北大学控制理论与控制工程硕士学位.主要研究方向为工业过程运行控制, 网络控制, 自适应动态规划, 强化学习.E-mail:JY369356904@163.com

    柴天佑  中国工程院院士, 东北大学教授, IEEE Fellow, IFAC Fellow.1985年获得东北大学博士学位.主要研究方向为自适应控制, 智能解耦控制, 流程工业综台自动化理论、方法与技术.E-mail:tychai@mail.neu.edu.cn

    通讯作者:

    范家璐  东北大学流程工业综合自动化国家重点实验室副教授.2011年获得浙江大学博士学位(与美国宾夕法尼亚州立大学联合培养).主要研究方向为工业过程运行控制, 工业无线传感器网络与移动社会网络.本文通信作者.E-mail:jlfan@mail.neu.edu.cn

  • 本文责任编委 侯忠生

Data-driven Dual-rate Control for Mixed Separation Thickening Process in a Wireless Network Environment

Funds: 

Supported by Natural Science Foundations of China 61333012

Fundamental Research Funds for the Central Universities N160804001

Supported by Natural Science Foundations of China 61333012

Supported by Natural Science Foundations of China 61333012

More Information
    Author Bio:

      Master student at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. Her research interest covers industrial process operational control, networked control and reinforcement learning

      Ph. D. candidate at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. He received his master degree in control theory and engineering from Northeastern University in 2016. His research interest covers industrial process operational control, networked control, adaptive dynamic programming and reinforcement learning

      Academician of Chinese Academy of Engineering, professor at Northeastern University, IEEE Fellow, IFAC Fellow. He received his Ph. D. degree from Northeastern University in 1985. His research interest covers adaptive control, intelligent decoupling control, and integrated automation theory, method and technology of industrial process

    Corresponding author: FAN Jia-Lu   Associate professor at the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. She received her Ph. D. degree from Zhejiang University in 2011. She was a Visiting Scholar with the Pennsylvania State University during 2009 ~ 2010. Her research interest covers networked operational control, industrial wireless sensor networks and mobile social networks. Corresponding author of this paper
  • 摘要: 无线网络环境下赤铁矿混合选别浓密过程控制问题是以底流矿浆泵频率为内环输入,以底流矿浆流量为内环输出外环输入,以底流矿浆浓度为外环输出的非线性串级工业过程控制问题.其外环反馈回路存在丢包,且模型参数难以辨识,故本文利用工业运行过程的在线数据,设计不依赖模型参数的跟踪控制器.首先,利用浓密过程运行在工作点附近的特点进行线性化,对流量过程设计Q-学习控制器,保证流量过程能够跟踪给定的流量设定值;然后采用提升技术,得到统一时间尺度的以底流矿浆流量设定值为输入,以底流矿浆浓度为输出的被控对象;最后,考虑到在无线网络环境下浓度过程存在反馈丢包,当前的状态可能无法获得,故采用史密斯预估器的思想,利用历史的数据估计系统当前的状态,设计丢包Q-学习设定值控制器为流量过程提供最优设定值.通过仿真实验验证所提算法的有效性.
    1)  本文责任编委 侯忠生
  • 图  1  混合选别浓密过程

    Fig.  1  he mixed separation thickening process

    图  2  数据驱动的无线网络下浓密过程的控制结构图

    Fig.  2  Structure diagram of data-driven for MSTP under wireless network environment

    图  3  浓密过程中浓度、流量的跟踪曲线以及底流泵转速的输入的曲线

    Fig.  3  The tracing result of the slurry concentration and the slurry flow-rate, and the input of the frequency of slurry pump

    图  4  流量过程控制增益K的收敛过程

    Fig.  4  Convergence of K to its optimal value K*

    图  5  流量过程Q-学习的结果

    Fig.  5  The result of the slurry flow-rate process during the Q-learning process

    图  6  浓度过程控制增益$\tilde L$的收敛过程

    Fig.  6  Convergence of $\tilde L$to its optimal value $\tilde L$*

    图  7  浓度过程丢包Q-学习的结果

    Fig.  7  The result of the slurry concentration process during the dropout Q-learning process

    图  8  浓度过程Q-学习的结果

    Fig.  8  The result of the slurry concentration process during the Q-learning process

    图  9  对比实验2的仿真结果图

    Fig.  9  The result of experiment 2

    图  10  增大Q2仿真结果图

    Fig.  10  The result of increasing Q2

    表  1  浓密过程符号表

    Table  1  Mixed separation thickening process symbol table

    符号 物理含义 符号 物理含义
    $S$ 浓密机横截面积 $\frac{{\Delta \rho (t)}}{{g\rho (\cdot)}}$ 泵两端管路单位重量
    矿浆的势能差
    $\mu$ 介质的粘度 $D$ 阻力损失
    $p$ 平均浓度系数 $k_i$, $\bar{K}$ 与浓密机结构有关的常数
    $p _s$ 矿浆内固体密度 $g$ 重力加速度
    $p _l$ 矿浆内液体密度 $\theta(t)$ 干扰
    $k_{0}$ 静态放大系数 $h(\cdot)$ 泥层界面高度
    $\tau$ 时间常数 ${v_p}(\cdot)$ 矿浆颗粒沉降速度
    ${\varphi _1}$ 浮选中矿矿浆浓度 ${q_1}$ 浮选中矿流量
    ${\varphi _2}$ 污水浓度 ${q_2}$ 污水流量
    ${\varphi _3}$ 磁选精矿矿浆浓度 ${q_3}$ 磁选精矿矿浆流量
    下载: 导出CSV

    表  2  对比实验2和3评价指标

    Table  2  Performance index of comparison experiment

    IAE MSE
    本文$Q_2$ 8.4224 0.0191
    未丢包 8.4093 0.0190
    增大$Q_2$ 0.0418 6.63$\times 10^{-7}$
    下载: 导出CSV
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  • 收稿日期:  2018-04-10
  • 录用日期:  2018-07-02
  • 刊出日期:  2019-06-20

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