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云控制系统不确定性分析与控制器设计方法

关守平 王梁

关守平, 王梁. 云控制系统不确定性分析与控制器设计方法. 自动化学报, 2022, 48(11): 2677−2687 doi: 10.16383/j.aas.c190529
引用本文: 关守平, 王梁. 云控制系统不确定性分析与控制器设计方法. 自动化学报, 2022, 48(11): 2677−2687 doi: 10.16383/j.aas.c190529
Guan Shou-Ping, Wang Liang. Uncertainty analysis of cloud control system with its controller design. Acta Automatica Sinica, 2022, 48(11): 2677−2687 doi: 10.16383/j.aas.c190529
Citation: Guan Shou-Ping, Wang Liang. Uncertainty analysis of cloud control system with its controller design. Acta Automatica Sinica, 2022, 48(11): 2677−2687 doi: 10.16383/j.aas.c190529

云控制系统不确定性分析与控制器设计方法

doi: 10.16383/j.aas.c190529
基金项目: 国家自然科学基金(62173072) 资助
详细信息
    作者简介:

    关守平:东北大学信息科学与工程学院教授. 1995年获得东北大学工业自动化系博士学位. 主要研究方向为复杂工业过程建模, 优化与控制, 网络与云控制, 智能控制. E-mail: guanshouping@ise.neu.edu.cn

    王梁:北京航空航天大学自动化科学与电气工程学院博士研究生. 2019年获得东北大学信息科学与工程学院自动化系学士学位. 主要研究方向为飞行器控制, 网络与云控制. 本文通信作者.E-mail: wang_liang@buaa.edu.cn

Uncertainty Analysis of Cloud Control System With Its Controller Design

Funds: Supported by National Natural Science Foundation of China (62173072)
More Information
    Author Bio:

    GUAN Shou-Ping Professor at the College of Information Science and Engineering, Northeastern University. He received his Ph.D. degree from the Department of Industrial Automation, Northeastern University in 1995. His research interest covers industrial process modeling, optimization and control, networked and clouded control, and intelligent control

    WANG Liang Ph.D. candidate at the School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics. He received his bachelor degree from the College of Information Science and Engineering, Northeastern University in 2019. His research interest covers aircraft control, networked and clouded control. Corresponding author of this paper

  • 摘要: 云控制系统(Cloud control system, CCS)是云计算与物理系统的融合, 由于云计算中资源是动态的, 因此云计算的加入使得云控制系统具有很大的不确定性. 本文给出一种典型的云控制系统结构, 通过将不确定性划分为云端不确定性和网络端不确定性, 有效简化了云控制系统不确定特性分析和建模. 针对典型的时延不确定性问题, 将云控制系统时延划分为云端时延和网络端时延, 进行了MapReduce模型下多计算节点云端时延分析, 同时进行了云控制结构下网络端时延分析, 两者结合实现了云控制系统的前向通道和反馈通道的时延建模. 基于所建立的云控制系统时延模型, 应用极点配置方法设计了云控制器算法, 包括观测器的设计和控制律的设计, 从而保证了闭环系统的稳定性. 对本文设计的云控制器算法进行了仿真验证, 结果表明考虑时延特性的控制器设计明显提升了云控制系统的控制性能.
  • 图  1  常规控制系统结构

    Fig.  1  The structure of conventional control system

    图  2  云控制系统结构

    Fig.  2  The structure of CCS

    图  3  云控制系统实现结构

    Fig.  3  The realization structure of CCS

    图  4  云控制系统理论结构模型

    Fig.  4  The theoretical structural model of CCS

    图  5  云控制系统不确定性分解图

    Fig.  5  The uncertainty division diagrgam of CCS

    图  6  MapReduce任务执行架构

    Fig.  6  The task execution architecture under MapReduce

    图  7  云端基于MapReduce任务执行模型

    Fig.  7  The task execution model under MapReduce in the cloud-end

    图  8  一般网络传输时延组成图

    Fig.  8  The delay composition graph of general network transmission

    图  9  云控制系统网络端传输时延组成图

    Fig.  9  The delay composition graph of the network-end in the CCS

    图  10  云控制系统时延模型结构

    Fig.  10  The structure of CCS time-delay model

    图  11  带有控制器的云控制系统结构

    Fig.  11  The structure of CCS with controller

    图  12  云特性影响控制效果对比图

    Fig.  12  The contrast graph of cloud characteristics influencing control effect

    图  13  短时延云特性控制器控制效果对比图

    Fig.  13  Contrast graph of control effect of short time-delay cloud characteristic controller

    图  14  长时延云特性控制器控制效果对比图

    Fig.  14  Contrast graph of control effect of long time-delay cloud characteristic controller

    表  1  愿意节点列表

    Table  1  The list of willing modes

    节点 IP 地址 优先级 排序
    $C_{1}$ $Add_{1}$ $S_{1}$ 1
    $C_{2}$ $Add_{2}$ $S_{1}$ 2
    $\vdots $ $\vdots $ $\vdots $ $\vdots $
    $C_{g}$ $Add_{g}$ $S_{g}$ $g$
    $\vdots $ $\vdots $ $\vdots $ $\vdots $
    $C_{M}$ $Add_{M}$ $S_{M}$ $M$
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-07-15
  • 录用日期:  2019-10-11
  • 网络出版日期:  2022-08-08
  • 刊出日期:  2022-11-22

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