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云控制系统并行任务分配优化算法与并联控制

王彩璐 陶跃钢 杨鹏 刘作军 周颖

王彩璐, 陶跃钢, 杨鹏, 刘作军, 周颖. 云控制系统并行任务分配优化算法与并联控制. 自动化学报, 2017, 43(11): 1973-1983. doi: 10.16383/j.aas.2017.c160504
引用本文: 王彩璐, 陶跃钢, 杨鹏, 刘作军, 周颖. 云控制系统并行任务分配优化算法与并联控制. 自动化学报, 2017, 43(11): 1973-1983. doi: 10.16383/j.aas.2017.c160504
WANG Cai-Lu, TAO Yue-Gang, YANG Peng, LIU Zuo-Jun, ZHOU Ying. Parallel Task Assignment Optimization Algorithm and Parallel Control for Cloud Control Systems. ACTA AUTOMATICA SINICA, 2017, 43(11): 1973-1983. doi: 10.16383/j.aas.2017.c160504
Citation: WANG Cai-Lu, TAO Yue-Gang, YANG Peng, LIU Zuo-Jun, ZHOU Ying. Parallel Task Assignment Optimization Algorithm and Parallel Control for Cloud Control Systems. ACTA AUTOMATICA SINICA, 2017, 43(11): 1973-1983. doi: 10.16383/j.aas.2017.c160504

云控制系统并行任务分配优化算法与并联控制

doi: 10.16383/j.aas.2017.c160504
基金项目: 

国家自然科学基金 60774007

国家自然科学基金 61305101

详细信息
    作者简介:

    王彩璐  河北工业大学控制科学与工程学院博士研究生.主要研究方向为离散事件系统控制与优化.E-mail:cailu_wang@163.com

    杨鹏  河北工业大学博士.河北工业大学控制科学与工程学院教授.主要研究方向为系统建模与控制, 康复机器人和智能假肢.E-mail:yangp@hebut.edu.cn

    刘作军  南开大学博士.河北工业大学控制科学与工程学院教授.主要研究方向为复杂系统控制与优化.E-mail:liuzuojun@hebut.edu.cn

    周颖  北京科技大学博士.河北工业大学控制科学与工程学院副教授.主要研究方向为智能控制与模式识别.E-mail:zhouying2007@163.com

    通讯作者:

    陶跃钢  中国科学院博士.河北工业大学控制科学与工程学院教授.主要研究方向为离散事件系统建模, 控制与优化.本文通信作者.E-mail:yuegangtao@hebut.edu.cn

Parallel Task Assignment Optimization Algorithm and Parallel Control for Cloud Control Systems

Funds: 

National Natural Science Foundation of China 60774007

National Natural Science Foundation of China 61305101

More Information
    Author Bio:

     Ph. D. candidate at the School of Control Science and Engineering, Hebei University of Technology. Her research interest covers control and optimization of discrete-event systems

     Ph. D. from Hebei University of Technology. Professor at the School of Control Science and Engineering, Hebei University of Technology. His research interest covers system modeling and control, service robot, and intelligent prothesis

     Ph. D. from Nankai University. Professor at the School of Control Science and Engineering, Hebei University of Technology. His research interest covers control and optimization of complex systems

     Ph. D. from University of Science and Technology Beijing. Associate professor at the School of Control Science and Engineering, Hebei University of Technology. Her research interest covers intelligent control and pattern recognition

    Corresponding author: TAO Yue-Gang  Ph. D. from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences. Professor at the School of Control Science and Engineering, Hebei University of Technology. His research interest covers modeling, control and optimization of discrete-event systems. Corresponding author of this paper
  • 摘要: 利用Petri网模拟云控制系统的并行处理过程,引入并行处理系统的时钟周期、吞吐率和任务完成时间性能指标,运用极大-加代数方法分析和优化云控制系统并行处理性能.采用子过程细分的优化方式,通过求解一类最优控制问题,设计并行任务分配优化方案,以保证任务完成时间最短,并给出计算最短任务完成时间的有效算法.同时,采用重复设置多套瓶颈段并联的方式提高并行处理能力,并运用Petri网实现瓶颈子过程的并联控制,且给出并联控制在协同云控制系统中的一个应用.
    1)  本文责任编委 苏宏业
  • 图  1  云控制系统[2]

    Fig.  1  Cloud control systems[2]

    图  2  指令级并行处理

    Fig.  2  Instruction level parallel processing

    图  3  并行处理系统的Petri网模型

    Fig.  3  Petri net of the parallel processing system

    图  4  矩阵${A}$的前趋图$\mathcal{G}({A})$

    Fig.  4  Precedence graph of matrix ${A}$

    图  5  指令相关

    Fig.  5  Instruction dependency

    图  6  发生指令相关的并行处理系统的Petri网模型

    Fig.  6  Petri net of the parallel processing with instruction dependency

    图  7  矩阵${B}$的前趋图$\mathcal{G}({B})$

    Fig.  7  Precedence graph of matrix ${B}$

    图  8  瓶颈子过程细分

    Fig.  8  Segmentation of the bottleneck sub-process

    图  9  瓶颈子过程并联

    Fig.  9  Parallel connection of bottleneck sub-processes

    图  10  子过程并联的Petri网模型

    Fig.  10  Petri net of parallel connection of sub-processes

    图  11  不同时刻并联系统的状态描述

    Fig.  11  State descriptions of the parallel system at various moments

    图  12  三套瓶颈段并联

    Fig.  12  Parallel connection with three bottleneck sub-processes

    图  13  三套瓶颈段并联的Petri网模型

    Fig.  13  Petri net of parallel connection with three bottleneck sub-processes

    图  14  协同云控制系统的框架图[2]

    Fig.  14  The framework of cooperative cloud control system[2]

    图  15  控制任务并联

    Fig.  15  Parallel connection of control tasks

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出版历程
  • 收稿日期:  2016-06-30
  • 录用日期:  2016-11-17
  • 刊出日期:  2017-11-20

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