基于云控制系统的监控系统设计与实现
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摘要
基于云计算的控制系统(简称云控制系统)由以云节点形式存在的控制器、传感器和执行器组成,各节点之间通过网络交换数据.针对云控制系统中云节点分布式运行和矩阵类型数据被广泛使用使系统运行状态难以观测的问题,设计并实现了一种云监控系统.该系统采用客户端/服务器架构,使用重构技术将性能稳定、功能丰富的控件以API的形式集成在系统中,满足了自定义监控界面的需求,提高了系统的可靠性.基于TCP/IP协议实现同云节点的双向通信,在获得云节点数据的同时支持数据在线调整,以达到连续监控的目的.通过设置线程池,以异步通信的方式实时获取并显示云节点的数据,达到监控与云节点数据同步.针对云计算中被广泛使用的矩阵类型数据,提供全元素的监控支持.最后,以直流电机系统为被控对象,基于阿里云平台的动态仿真和本地云平台的实际实验,验证了所提出云监控系统的可行性和实用性,促进和完善控制系统与云计算结合在实际中的应用.
The control system based on cloud computing(cloud control systems) includes controllers, actuators and sensors which exist as cloud nodes. Information is exchanged among cloud nodes via the network. To solve the problem that the cloud control system is difficult to observe, which results from the wide application of the cloud node distributed running and matrix type data, a cloud supervisory system is designed and implemented. In this paper, the client/server architecture of this system is described. The stability and richly functional controls are integrated into the system as the API by reconstructing, which meet the requirement of customize supervisory interface and improve the reliability of systems. The system also supports tuning parameters online to achieve consecutive observation through the duplex channel based on the TCP/IP protocol. The data of cloud nodes is obtained and displayed in real time through asynchronous communication. All the elements of the matrix which is a common data type in cloud control systems are supported to supervise. Finally, for the motor plant, the Internet cloud-based dynamic simulation and Intranet cloud-based experiment demonstrate the feasibility and practicability of the designed system, and further promote and improve the control system combined with cloud computing in practical application.
The control system based on cloud computing(cloud control systems) includes controllers, actuators and sensors which exist as cloud nodes. Information is exchanged among cloud nodes via the network. To solve the problem that the cloud control system is difficult to observe, which results from the wide application of the cloud node distributed running and matrix type data, a cloud supervisory system is designed and implemented. In this paper, the client/server architecture of this system is described. The stability and richly functional controls are integrated into the system as the API by reconstructing, which meet the requirement of customize supervisory interface and improve the reliability of systems. The system also supports tuning parameters online to achieve consecutive observation through the duplex channel based on the TCP/IP protocol. The data of cloud nodes is obtained and displayed in real time through asynchronous communication. All the elements of the matrix which is a common data type in cloud control systems are supported to supervise. Finally, for the motor plant, the Internet cloud-based dynamic simulation and Intranet cloud-based experiment demonstrate the feasibility and practicability of the designed system, and further promote and improve the control system combined with cloud computing in practical application.
引文
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[24]桂卫华.基于机器视觉的矿物浮选过程监控技术研究进展[J].自动化学报,2013,39(11):1879-1888.(Gui W H.Machine-vision-based online measuring and controlling technologies for mineral flotation-Areview[J].Acta Automatica Sinica,2013,39(11):1879-1888.)
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[2]陈康,郑纬民.云计算:系统实例与研究现状[J].软件学报,2009,20(5):1337-1348.(Chen K,Zheng W M.Cloud computing:System instances and current research[J].J of Software,2009,20(5):1337-1348.)
[3]Armbrust M,Fox A,Griffith R,et al.A view of cloud computing[J].Communications of the ACM,2010,53(4):50-58.
[4]Aceto G,Botta A,De Donato W,et al.Cloud monitoring:A survey[J].Computer Networks,2013,57(9):2093-2115.
[5]Fahad A M,Ahmed A A,Kahar M N M.The importance of monitoring cloud computing:An intensive review[C].Proc of the TENCON 2017 IEEE Region 10 Conf.Penang:IEEE,2017:2858-2863.
[6]Abrah?o S,Insfran E.Models@runtime for monitoring cloud services in Google app engine[C].Proc of the 2017IEEE World Congress on Services.Honolulu:IEEE,2017:30-35.
[7]Zou D,Zhang W,Qiang W,et al.Design and implementation of a trusted monitoring framework for cloud platforms[J].Future Generation Computer Systems,2013,29(8):2092-2102.
[8]Dong B T,Lee C M,Chiu Y C,et al.An intelligent embedded cloud monitoring system design[C].Proc of the 2017 Int Automatic Control Conf.Pingtung:IEEE,2017:1-4.
[9]Pingali S.Cloud computing and crowdsourcing for monitoring lakes in developing countries[C].Proc of the2016 IEEE Int Conf on Cloud Computing in Emerging Markets.Bangalore:IEEE,2016:161-163.
[10]Roopaei M,Rad P,Choo K K R.Cloud of things in smart agriculture:Intelligent irrigation monitoring by thermal imaging[J].IEEE Cloud Computing,2017,4(1):10-15.
[11]Abawajy J H,Hassan M M.Federated internet of things and cloud computing pervasive patient health monitoring system[J].IEEE Communications Magazine,2017,55(1):48-53.
[12]Yuanqing Xia.Cloud control systems[J].IEEE/CAA J of Automatica Sinica,2015,2(2):134-142.
[13]Yuanqing Xia,Yongming Qin,Di-Hua Zhai,et al.Further results on cloud control systems[J].Science China:Information Sciences,2016,59(7):232-236.
[14]Xiao Z,Xiao Y.Security and privacy in cloud computing[J].IEEE Communications Surveys and Tutorials,2013,15(2):843-859.
[15]Kapil D,Tyagi P,Kumar S,et al.Cloud computing:Overview and research issues[C].Proc of the 2017 Int Conf on Green Informatics.Fuzhou:IEEE,2017:71-76.
[16]Subashini S,Kavitha V.A survey on security issues in service delivery models of cloud computing[J].J of Network&Computer Applications,2011,34(1):1-11.
[17]Ma L,Xia Y,Ali Y,et al.Engineering problems in initial phase of cloud control system[C].Proc of the 36th Chinese Control Conf.Dalian:IEEE,2017:7892-7896.
[18]夏元清.云控制系统及其面临的挑战[J].自动化学报,2016,42(1):1-12.(Xia Y Q.Cloud control systems and their challenges[J].Acta Automatica Sinica,2016,42(1):1-12.)
[19]Xia Y Q,Xie W,Liu B,et al.Data-driven predictive control for networked control systems[J].Information Sciences,2013,235(6):45-54.
[20]Guo-Ping Liu.Predictive control of networked multiagent systems via cloud computing[J].IEEE Trans on Cybernetics,2017,47(8):1852-1859.
[21]王彩璐,陶跃钢,杨鹏,等.云控制系统并行任务分配优化算法与并联控制[J].自动化学报,2017,43(11):1973-1983.(Wang C L,Tao Y G,Yang P,et al.Parallel task assignment optimization algorithm and parallel control for cloud control systems[J].Acta Automatica Sinica,2017,43(11):1973-1983.)
[22]Rossigneux F,Lefevre L,Gelas J P,et al.A generic and extensible framework for monitoring energy consumption of OpenStack clouds[C].Proc of the 2014 IEEE 4th Int Conf on Big Data and Cloud Computing.Sydney:IEEE,2014:696-702.
[23]Wang J,Zhang Y.Monitoring system of machine tools based on the InTouch[C].Proc of the 2013 Int Conf on Mechanical and Automation Engineering.Jiujang:IEEE,2013:70-72.
[24]桂卫华.基于机器视觉的矿物浮选过程监控技术研究进展[J].自动化学报,2013,39(11):1879-1888.(Gui W H.Machine-vision-based online measuring and controlling technologies for mineral flotation-Areview[J].Acta Automatica Sinica,2013,39(11):1879-1888.)
[25]Yangang X,Han W,Xingqi L,et al.Monitor system design for machine electric spindle based on MCGS[C].Proc of the 2010 WASE Int Conf on Information Engineering.Beidaihe:IEEE,2010,3:248-252.
[26]Baoran An,Liu G P.Networked real-time controller based on PC/104[C].Proc of the 2012 10th World Congress on Intelligent Control and Automation.Beijing:IEEE,2012:831-834.
[27]Pang Z H,Liu G P.Model-based recursive networked predictive control[C].Proc of the 2010 IEEE Int Conf on Systems Man and Cybernetics.Istanbul:IEEE,2010:1665-1670.