一种基于边界特征线且特征点可变的二阶非线性离散跟踪微分器及在测速定位系统中的应用

利用状态反推方法确定最速离散二阶系统的线性区域的边界特征线及控制特征线,以相平面上的点及边界曲线、控制线的相对位置按线性规则确定控制量的大小,区分可达区与线性区,并依此构造最速分段线性函数形式的跟踪微分器（Tracking-differentiator,TD）,这种算法可以方便地修改特征点,适应能力强,而且运算中不包含任何根号运算,使得控制综合函数的形式极大简化,有利于工程实现.对正弦信号及方波信号的仿真结果表明了上述结论的合理性.验证了特征点分段线性算法在适当修改特征点后得到的TD,与经典TD以及它的线性近似进行比较,跟踪能力和微分提取能力都得到了较大的提高.应用移动平均算法构成的跟踪微分器组,具有相位补偿功能,适当选取TD参数后,得到了滤波能力强、相位特性良好的滤波器,并应用于基于长定子齿槽检测的永磁电动磁悬浮列车的测速定位系统中.实验结果表明,本文提出的简化TD按相位补偿确定的方案能有效滤除脉冲和扰动等噪声,对过轨道接缝时的畸变信号进行修复,边界容易修改,算法简单有效,实时性强,易于工程实现.     

一类不确定非线性系统的自适应输出反馈镇定

研究了一类不确定非线性系统的全局自适应输出反馈镇定问题. 由于不确定控制系数和未知线性增长率的存在, 这个问题比较复杂且很难解决. 本文引入一个新的在线调节的动态增益, 并基于此设计了高增益K-滤波器用于重构系统的状态. 然后, 受广义控制方法的启发, 发展了反推法并设计了自适应输出反馈镇定控制器. 结果表明, 通过选择恰当的设计参数可以保证闭环系统的全局稳定性. 给出的仿真算例验证了本文理论结果的正确性.     

系统的增速依赖于不可测状态非线性系统全局输出反馈渐近镇定

研究了一类非线性增长速度依赖于不可测状态且有稳定零动态非线性系统的全局输出反馈渐近稳定控制问题. 因所研究的系统隐含有零动态, 所以首先定义了一系列新的线性变换, 从而成功地分离出原系统的零动态, 得到了便于输出反馈设计的新系统. 然后给出了变换后系统的较为简洁的输出反馈控制设计过程, 并且, 闭环系统的渐近稳定性可由所导出矩阵的正定性来保证. 最后, 仿真算例验证了文中理论结果的正确性.     

Output-feedback adaptive stabilization control design for non-holonomic systems with strong non-linear drifts

This paper investigates the problem of output-feedback adaptive stabilization control design for non-holonomic chained systems with strong non-linear drifts, including modelled non-linear dynamics, unmodelled dynamics, and those modelled but with unknown parameters. An observer and an estimator are introduced for state and parameter estimates, respectively. By using the integrator backstepping approach and based on the observer and parameter estimator, a constructive design procedure for output-feedback adaptive stabilization control is given. It is shown that, under some conditions, the control design ensures the closed-loop system is globally asymptotically stable when there is no non-linear drift in the first subsystem, and semiglobally asymptotically stable, otherwise. An example is given to show the effectiveness of the theory.     

A Case for Adaptive Resource Management in Alibaba Datacenter Using Neural Networks

Both resource efficiency and application QoS have been big concerns of datacenter operators for a long time,but remain to be irreconcilable.High resource utilization increases the risk of resource contention between co-located workload,which makes latency-critical(LC)applications suffer unpredictable,and even unacceptable performance.Plenty of prior work devotes the effort on exploiting effective mechanisms to protect the QoS of LC applications while improving resource efficiency.In this paper,we propose MAGI,a resource management runtime that leverages neural networks to monitor and further pinpoint the root cause of performance interference,and adjusts resource shares of corresponding applications to ensure the QoS of LC applications.MAGI is a practice in Alibaba datacenter to provide on-demand resource adjustment for applications using neural networks.The experimental results show that MAGI could reduce up to 87.3%performance degradation of LC application when co-located with other antagonist applications.     

双联曲柄连杆机构在啤酒糖化设备中的应用

啤酒糖化器过滤槽的出糟阀,过去一般都采用大口径闸阀或液压顶升阀来作为过滤槽的出糟阀。经过比较,认为大口径门阀和液压顶升阀,不但成本高,而且液压顶升阀的制造难度大,又要配备液压装置,大口径闸阀对麦汁的充分利用上有不足之处。所以我们设想了另一种方案,采用组合的曲柄连杆机构,我们称之为双联曲柄连杆机构,用于过滤槽的出糟阀中,经过七年的生产验证,证明性能良好,是一种结构简单,使用维修方便,经济效果好的机构。     

Electrochemical behavior of tungsten in （NaCI-KCI-NaF-W03） molten salt

Abstract The electrochemical reaction mechanism and electrocrystaUization process of tungsten in the NaCl- KCl-NaF-WO3 molten salt were investigated at 973 K （700℃） by means of cyclic voltammetry, chronopotentiometry, and chronoamperometry techniques. The results show that the electrochemical reaction process of tungsten in the NaCl-KCl-NaF-WO3 molten salt system is a quasireversible process mix-controlled by ion diffusion rate and electron transport rate. Tungsten ion in this system is reduced to W（0） in two steps. The electrocrystallization process of tungsten is found to be an instantaneous, hemispheroid three-dimensional nucleation process and the tungsten ion diffusion coefficient of 2.361 × 10^-4 cm2.s^-1 is obtained at experimental conditions.     

高阶非线性不确定系统的自适应实际输出跟踪控制

本文研究了一类高阶非线性不确定系统的自适应实际输出跟踪控制问题, 该问题在未知控制系数的下界精确知道的假设下已经得到了研究. 基于新的鲁棒自适应控制和连续控制思想, 成功去除该假设条件. 进而应用增加幂次积分的方法, 给出了构造连续自适应实际输出跟踪控制器的系统化方法. 该控制器确保闭环系统的所有状态全局稳定, 并且经过有限时间后, 跟踪误差可以被某一事先给定的任意正数界定. 最后, 通过一个仿真算例验证了理论结果的正确性.     

NfvInsight: A Framework for Automatically Deploying and Benchmarking VNF Chains

With the advent of virtualization techniques and software-defined networking (SDN), network function virtualization (NFV) shifts network functions (NFs) from hardware implementations to software appliances, between which exists a performance gap. How to narrow the gap is an essential issue of current NFV research. However, the cumbersomeness of deployment, the water pipe effect of virtual network function (VNF) chains, and the complexity of the system software stack together make it tough to figure out the cause of low performance in the NFV system. To pinpoint the NFV system performance, we propose NfvInsight, a framework for automatic deployment and benchmarking VNF chains. Our framework tackles the challenges in NFV performance analysis. The framework components include chain graph generation, automatic deployment, and fine granularity measurement. The design and implementation of each component have their advantages. To the best of our knowledge, we make the first attempt to collect rules forming a knowledge base for generating reasonable chain graphs. NfvInsight deploys the generated chain graphs automatically, which frees the network operators from executing at least 391 lines of bash commands for a single test. To diagnose the performance bottleneck, NfvInsight collects metrics from multiple layers of the software stack. Specifically, we collect the network stack latency distribution ingeniously, introducing only less than 2.2% overhead. We showcase the convenience and usability of NfvInsight in finding bottlenecks for both VNF chains and the underlying system. Leveraging our framework, we find several design flaws of the network stack, which are unsuitable for packet forwarding inside one single server under the NFV circumstance. Our optimization for these flaws gains at most 3x performance improvement.     

Labeled von Neumann Architecture for Software-Defined Cloud

As cloud computing is moving forward rapidly, cloud providers have been encountering great challenges: long tail latency, low utilization, and high interference. They intend to co-locate multiple workloads on a single server to improve the resource utilization. But the co-located applications suffer from severe performance interference and long tail latency, which lead to unpredictable user experience. To meet these challenges, software-defined cloud has been proposed to facilitate tighter coordination among application, operating system and hardware. Users’ quality of service (QoS) requirements could be propagated all the way down to the hardware with differential management mechanisms. However, there is little hardware support to maintain and guarantee users’ QoS requirements. To this end, this paper proposes Labeled von Neumann Architecture (LvNA), which introduces a labelling mechanism to convey more software’s semantic information such as QoS and security to the underlying hardware. LvNA is able to correlate labels with various entities, e.g., virtual machine, process and thread, and propagate labels in the whole machine and program differentiated services based on rules. We consider LvNA to be a fundamental hardware support to the software-defined cloud.