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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.Abstract: Cloud control system (CCS) is the integration of cloud computing and physical systems. Due to the dynamic resources added, the CCS possesses more uncertainties than other control systems. In this paper, a typical structure of CCS is presented, where the uncertainties are divided into the cloud uncertainty and the network uncertainty, to effectively simply the uncertainty analysis and modeling. Aiming at the typical uncertainty of time-delay, it is divided into the cloud-end time-delay and the network-end time-delay. The first one is carried out under the condition of multi-computing nodes in the MapReduce model, and the second one is also proceeded under the cloud control structure, then the CCS model with the forward channel time-delay and the feedback channel time-delay is realized by superimposing the above mentioned two parties. The controller is then designed based on the established time-delay model using the pole placement method, including the observer and the control law, thus the stability of closed-loop control for CCS is guaranteed. The simulation results show that the control performance of CCS is improved obviously by considering the time-delay characteristics.
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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 $ $C_{M}$ $Add_{M}$ $S_{M}$ $M$ -
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