2.845

2023影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于CPS框架的微粉生产过程多模型自适应控制

李晓理 王康 于秀明 苏伟

李晓理, 王康, 于秀明, 苏伟. 基于CPS框架的微粉生产过程多模型自适应控制. 自动化学报, 2019, 45(7): 1354-1365. doi: 10.16383/j.aas.2018.c180387
引用本文: 李晓理, 王康, 于秀明, 苏伟. 基于CPS框架的微粉生产过程多模型自适应控制. 自动化学报, 2019, 45(7): 1354-1365. doi: 10.16383/j.aas.2018.c180387
LI Xiao-Li, WANG Kang, YU Xiu-Ming, SU Wei. CPS-based Multiple Model Adaptive Control of GGBS Production Process. ACTA AUTOMATICA SINICA, 2019, 45(7): 1354-1365. doi: 10.16383/j.aas.2018.c180387
Citation: LI Xiao-Li, WANG Kang, YU Xiu-Ming, SU Wei. CPS-based Multiple Model Adaptive Control of GGBS Production Process. ACTA AUTOMATICA SINICA, 2019, 45(7): 1354-1365. doi: 10.16383/j.aas.2018.c180387

基于CPS框架的微粉生产过程多模型自适应控制

doi: 10.16383/j.aas.2018.c180387
基金项目: 

北京市科技重大专项 Z181100003118012

国家重点研发计划项目 2018YFB1702704

国家重点研发计划项目 2018YFC1602704

国家自然科学基金 61873006

北京市科技新星交叉学科项目 Z161100004916041

国家自然科学基金 61673053

国家自然科学基金 61473034

详细信息
    作者简介:

    王康   北京工业大学信息学部博士后.分别于2012年和2018年获得北京科技大学学士学位和博士学位.主要研究方向为最优控制, 智能控制.E-mail:wangkang@bjut.edu.cn

    于秀明   中国电子技术标准化研究院软件工程与评估中心副主任.2004年获北京航空航天大学学士学位, 2013年获对外经济贸易大学硕士学位.主要研究方向为信息物理系统, 工业互联网以及两化融合.E-mail:yuxiuming@cesi.cn

    苏伟   中国电子技术标准化研究院助理工程师.分别于2014年和2016年获得延边大学学士学位和硕士学位.主要研究方向为信息物理系统, 智能制造, 两化融合, 工业互联网平台.E-mail:suwei@cesi.cn

    通讯作者:

    李晓理   北京工业大学信息学部教授.2000年获得东北大学博士学位.主要研究方向为复杂系统的建模优化与控制, 智能控制.本文通信作者.E-mail:lixiaolibjut@bjut.edu.cn

CPS-based Multiple Model Adaptive Control of GGBS Production Process

Funds: 

Beijing Major Science and Technology Special Projects Z181100003118012

National Key Research and Development Project 2018YFB1702704

National Key Research and Development Project 2018YFC1602704

National Natural Science Foundation of China 61873006

Beijing Nova Programme Interdisciplinary Cooperation Project Z161100004916041

National Natural Science Foundation of China 61673053

National Natural Science Foundation of China 61473034

More Information
    Author Bio:

       Postdoctor at Faculty of Information Technology, Beijing University of Technology. He received his bachelor degree and Ph. D. degree from the University of Science and Technology Beijing in 2012 and 2018, respectively. His research interest covers optimal control and intelligent control

       Deputy director of Software Engineering and Appraisal Center, China Electronic Standardization Institute. She received her bachelor degree from Beijing University of Aeronautics and Astronautics in 2004, and master degree from University of International Business and Economics in 2013. Her research interest covers cyber-physical system, industrial internet, integration of informatization and industrialization

       Assistant engineer at China Electronic Standardization Institute. He received his bachelor degree and master degree from Yanbian University in 2014 and 2016, respectively. His research interest covers cyber-physical system, intelligent manufacturing, integration of informatization and industrialization, industrial internet platform

    Corresponding author: LI Xiao-Li   Professor at the Faculty of Information Technology, Beijing University of Technology. He received his Ph. D degree from Northeastern University in 2000. His research interest covers modeling, control and optimization of complex system, and intelligent control. Corresponding author of this paper
  • 摘要: 针对矿渣微粉(Ground granulated blast-furnace slag,GGBS)生产这一多变量、强耦合、多工况的复杂非线性过程,本文根据大量生产数据,提炼出矿渣微粉生产过程的三个典型工况.求解多工况多目标优化问题以求得最优设定值.建立多工况下的递归神经网数据驱动模型,并采用自适应动态规划方法,建立多个控制器,结合加权多模型控制,实现矿渣微粉生产过程在多工况切换情况下的自适应控制.通过过程运行优化、跟踪控制优化、通讯、工业以太网等信息资源与矿渣微粉生产物理资源之间的融合,构建基于信息物理系统(Cyber-physical system,CPS)的矿渣微粉生产优化控制系统.实验分析表明,本文提出的基于CPS的多模型自适应控制器,能够有效实现多工况条件下矿渣微粉生产过程的自适应控制,减小超调量,提高控制品质.
    1)  本文责任编委 乔俊飞
  • 图  1  信息物理系统

    Fig.  1  Cyber-physical system

    图  2  矿渣微粉生产系统流程图

    Fig.  2  Flow chart of GGBS production process

    图  3  优化控制结构图

    Fig.  3  Structure of optimal contol

    图  4  矿渣微粉生产最优控制系统CPS硬件结构

    Fig.  4  The CPS hardware structure of GGBS production optimal control system

    图  5  微粉参数作用机理

    Fig.  5  Interaction among parameters of GGBS production process

    图  6  多目标优化设定值流程

    Fig.  6  Flow chart of set-point optimization using multi-objective optimization algorithm

    图  7  概率加权多模型ADP结构

    Fig.  7  Structure of weighted multiple model ADP

    图  8  矿渣微粉生产过程CPS框架

    Fig.  8  The CPS structure of GGBS production process

    图  9  Pareto最优解集

    Fig.  9  Obtained Pareto solutions

    图  10  采用控制器3时的质量曲线

    Fig.  10  The quality curve using controller 3

    图  11  多模型ADP微粉质量曲线

    Fig.  11  The quality curve using multiple model ADP

    图  12  多模型ADP控制输入曲线

    Fig.  12  The curve of control using multiple model ADP

    图  13  多模型ADP工况切换识别

    Fig.  13  Working condition identification using the multiple model ADP algorithm

    图  14  矿渣微粉粉磨系统运行数据

    Fig.  14  Operation data of GGBS production system

    表  1  各控制变量允许变化范围

    Table  1  Permitted range for each variable

    名称 变量 最小值 最大值 单位
    喂料量 $u_1$ 75 115 $10^3 {\rm kg/h}$
    选粉机转速 $u_2$ 850 1 250 ${\rm r/min}$
    入磨风温 $u_3$ 190 300
    冷风阀开度 $u_4$ 30 95 $\%$
    下载: 导出CSV

    表  2  微粉厂3号矿渣微粉生产线生产工况1运行数据

    Table  2  Process data for GGBS production line 3 in condition 1

    编号 喂料量 电机转速 入磨风温 冷风阀开度 比表面积 产品产量
    ($10^3$ kg/h) (r/min) (℃) (%) ($\rm{m}^2$/kg) ($10^3$ kg/h)
    1 101.76 1 090.31 240.71 63.46 451.75 95.12
    2 103.44 1 089.70 241.10 64.96 436.13 102.17
    3 108.15 1 099.65 265.08 57.08 419.30 106.16
    $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$
    198 108.95 1 089.32 249.54 65.82 435.96 102.49
    199 103.84 1 089.06 244.98 66.06 431.47 102.40
    200 102.12 1 119.46 266.82 61.03 427.28 92.61
    下载: 导出CSV

    表  3  微粉厂3号矿渣微粉生产线生产工况2运行数据

    Table  3  Process data for GGBS production line 3 in condition 2

    编号 喂料量 电机转速 入磨风温 冷风阀开度 比表面积 产品产量
    ($10^3$ kg/h) (r/min) (℃) (%) ($\rm{m}^2$/kg) ($10^3$ kg/h)
    1 84.24 1 249.09 234.88 63.80 439.75 79.33
    2 86.89 1 251.25 231.13 66.85 431.42 82.05
    3 82.54 1 249.28 241.51 63.24 428.76 76.55
    $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$
    198 84.94 1 159.96 228.21 65.72 427.26 80.49
    199 85.64 1 239.06 242.95 62.55 439.88 78.54
    200 85.42 1 248.68 233.01 69.12 424.21 78.75
    下载: 导出CSV

    表  4  微粉厂3号矿渣微粉生产线生产工况3运行数据

    Table  4  Process data for GGBS production line 3 in condition 3

    编号 喂料量 电机转速 入磨风温 冷风阀开度 比表面积 产品产量
    ($10^3$ kg/h) (r/min) (℃) (%) ($\rm{m}^2$/kg) ($10^3$ kg/h)
    1 104.09 1 015.28 216.60 61.44 435.62 96.91
    2 104.66 998.94 250.78 57.66 423.72 95.04
    3 102.15 1 000.03 237.65 59.70 445.55 94.23
    $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$ $\vdots$
    198 92.77 1 020.70 263.63 55.51 433.80 83.13
    199 106.54 1 011.66 236.26 62.65 443.47 97.64
    200 93.11 1 009.24 209.42 56.66 426.29 87.44
    下载: 导出CSV
  • [1] Saranya P, Nagarajan P, Shashikala A P. Eco-friendly GGBS concrete:a state-of-the-art review. IOP Conference Series:Materials Science and Engineering, 2018, 330(1):012057
    [2] Li X L, Jia C, Liu D X, Ding D W. Nonlinear adaptive control using multiple models and dynamic neural networks. Neurocomputing, 2014, 136:190-200 doi: 10.1016/j.neucom.2014.01.013
    [3] Li X L, Jia C, Wang K, Wang J. Trajectory tracking of nonlinear system using multiple series-parallel dynamic neural networks. Neurocomputing, 2015, 168:1-12 doi: 10.1016/j.neucom.2015.06.024
    [4] Wei Q L, Song R Z, Yan P F. Data-driven zero-sum neuro-optimal control for a class of continuous-time unknown nonlinear systems with disturbance using ADP. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(2):444-458 doi: 10.1109/TNNLS.2015.2464080
    [5] 王康, 李晓理, 贾超, 宋桂芝.基于自适应动态规划的矿渣微粉生产过程跟踪控制.自动化学报, 2016, 42(10):1542-1551 http://www.aas.net.cn/CN/abstract/abstract18941.shtml

    Wang Kang, Li Xiao-Li, Jia Chao, Song Gui-Zhi. Optimal tracking control for slag grinding process based on adaptive dynamic programming. Acta Automatica Sinica, 2016, 42(10):1542-1551 http://www.aas.net.cn/CN/abstract/abstract18941.shtml
    [6] 信息物理系统白皮书(2017).中国电子技术标准化研究院, 2017

    Cyber-physical systems white paper (2017). China Electronics Standardization Institute, 2017
    [7] Zhao H Z, Sun D H, Yue H, Zhao M, Cheng S L. Using CSTPNs to model traffic control CPS. IET Software, 2017, 11(3):116-125 doi: 10.1049/iet-sen.2016.0119
    [8] 马大中, 胡旭光, 孙秋野.基于大维数据驱动的油气管网泄漏监控模糊决策方法.自动化学报, 2017, 43(8):1370-1382 http://www.aas.net.cn/CN/abstract/abstract19111.shtml

    Ma Da-Zhong, Hu Xu-Guang, Sun Qiu-Ye. A large dimensional data-driven fuzzy detection method for oil-gas pipeline network leakage. Acta Automatica Sinica, 2017, 43(8):1370-1382 http://www.aas.net.cn/CN/abstract/abstract19111.shtml
    [9] Li D, Zhan M Y, Zhang X Z, Fang Z P, Liu H Q. ISAR imaging of nonuniformly rotating target based on the multicomponent CPS model under low SNR environment. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(3):1119-1135 doi: 10.1109/TAES.2017.2667538
    [10] Zhang Y, Qiu M K, Tsai C W, Hassan M M, Alamri A. Health-CPS:healthcare cyber-physical system assisted by cloud and big data. IEEE Systems Journal, 2017, 11(1):88-95 doi: 10.1109/JSYST.2015.2460747
    [11] Khan M U, Li S, Wang Q X, Shao Z L. CPS oriented control design for networked surveillance robots with multiple physical constraints. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2016, 35(5):778-791 doi: 10.1109/TCAD.2016.2524653
    [12] Higuera-Toledano M T, Risco-Martin J L, Arroba P, Ayala J L. Green adaptation of real-time web services for industrial CPS within a cloud environment. IEEE Transactions on Industrial Informatics, 2017, 13(3):1249-1256 doi: 10.1109/TII.2017.2693365
    [13] Chai T Y, Wu Z W, Wang H. A CPS based optimal operational control system for fused magnesium furnace. IFAC-PapersOnLine, 2017, 50(1):14992-14999 doi: 10.1016/j.ifacol.2017.08.2566
    [14] Pal R, Prasanna V. The STREAM mechanism for CPS security:the case of the smart grid. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2017, 36(4):537-550 doi: 10.1109/TCAD.2016.2565201
    [15] Wang K, Li X L. Multiple set-points tracking control based on online ADP. In: Proceedings of the 2016 Chinese Control and Decision Conference (CCDC). Yinchuan, China: IEEE, 2016. 1214-1219
    [16] Zhang X, Zhang H G, Sun Q Y, Luo Y H. Adaptive dynamic programming-based optimal control of unknown nonaffine nonlinear discrete-time systems with proof of convergence. Neurocomputing, 2012, 91:48-55 doi: 10.1016/j.neucom.2012.01.025
    [17] Wang K, Li X L, Jia C, Yang S X, Li M Q, Li Y. Multiobjective optimization of the production process for ground granulated blast furnace slags. Soft Computing, DOI: 10.1007/s00500-017-2761-x
  • 加载中
图(14) / 表(4)
计量
  • 文章访问数:  2498
  • HTML全文浏览量:  307
  • PDF下载量:  478
  • 被引次数: 0
出版历程
  • 收稿日期:  2018-05-31
  • 录用日期:  2018-09-12
  • 刊出日期:  2019-07-20

目录

    /

    返回文章
    返回