基于节点稳定度双路径应用层组播树构建算法

针对应用层组播树存在的稳定性的问题,在双路径组播方案的基础上,综合考虑节点度和节点在线时间对组播树构建的权重影响,定义节点稳定度,提出一种节点稳定度的双路径应用层组播树构建算法.在构建双路径组播树时,使节点稳定度高的叶子节点在第二棵组播树中距离源节点较近,并根据节点稳定度的改变动态调整双路径应用层组播树中节点的位置,使得节点退出或加入组播组时,不需要重新构建组播树也可以接收到传输的多媒体数据,从而降低组播树的中断次数,提高应用层组播稳定性,改善应用层组播的性能.通过计算机仿真,表明改进算法在组播节点动态改变时提高了组播树的稳定性,改善了性能,适合多媒体组播业务传输.     

基于遗传算法的可扩展应用层组播树构建

在应用层组播中,为降低节点的路径延时,通常采用遗传算法和启发式算法来减小组播树直径的方法,但在组播树具有大规模节点数时,遗传算法收敛时间长,而采用启发式算法难以在有约束条件下达到全局最优.本文在具有超节点的双层应用层组播模型基础上,提出了利用遗传算法构建出度受限最小带权路径延时生成树（MWPL-DC-ST）的生成算法GA-MWPL-DC-ST,利用该算法可在超节点上对双层组播树进行分布式构建,从而将求最优解问题的巨大计算量分担到多个超节点上.算法中的初始化、杂交和变异阶段采用启发式算法,对变异参数进行适应性调整,加快了算法的收敛速度.仿真试验表明,本文提出的双层应用层组播模型和GA-MWPL-DC-ST算法能得到比启发式算法更优的解,与采用单层模型的遗传算法相比较,显著降低了算法收敛时间,解决了遗传算法构建有大规模节点数的应用层组播树的可扩展性问题.     

基于节点稳定概率和链路贡献度的应用层组播树生成算法

组播技术从IP组播向应用层组播的发展,解决了IP组播部署难的问题.应用层组播依靠终端主机进行组播数据的转发,需要解决应用层组播的稳定性.最小延迟组播树的生成等问题.首先分析了影响应用层组播稳定和延时的3个因素:节点稳定概率、节点出度约束和节点间的通信延时.根据这些影响因素抽象出基于稳定概率的度约束边带权应用层组播树生成T-SDE模型,给出稳定度在T-SDE下的表达形式,并证明T-SDE问题属于NP-hard;其次通过分析节点对组播树稳定和延时的贡献,给出3种基于节点稳定概率和链路贡献度的T-SDE问题的近似解决算法;实验表明,该类算法生成的组播树在平均延时、最大延时和稳定度等方面有较大优势.     

应用层组播稳定性提高技术综述

互联网上组通信应用的日益普及和传统IP组播发展面临的困境导致应用层组播逐步受到广泛关注.将组播功能从路由器迁移到主机能够有效解决许多与IP组播相关的问题,但同时也带来一些新的挑战,如应用层组播稳定性问题.文中概述了应用层组播的数据传输模型、组播树构造算法和协议性能评价标准,阐述了应用层组播稳定性问题产生的原因,提出了衡量应用层组播稳定性的标准,分析了影响应用层组播稳定性的因素,根据影响因素将应用层组播稳定性提高技术分类为降低节点离开事件发生频率的方法、缩小节点离开事件影响范围的方法以及缩短节点离开事件发生后组播树恢复时间的方法,并介绍了各种应用层组播稳定性提高技术,展望了该领域未来的研究工作.     

一种高稳定性低延迟的应用层组播生成树算法

由于应用层组播技术依靠终端主机转发组播数据,任意中间节点的退出都将造成系统的稳定性问题。同时,应用层组播技术对延时有严格的要求。为了提高应用层组播系统的稳定性和数据传输效率,根据影响应用层组播稳定性和延时的因素,抽象出基于节点稳定概率的度约束的最小延时应用层组播生成树问题模型SDMD (Spanning tree based on stability probability,degree-constrained,and minimum diameter for ALM),并且证明了该问题属于NP-hard问题。为了解决该问题,给出了基于节点时间增益因子的TG-S近似算法。仿真实验表明,TG-S算法生成的组播树在平均延时、最大延时和累积中断次数等方面有明显优势。     

一种高稳定性应用层组播树构建算法

提出一种基于节点在线时间期望的应用层组播树构建算法(MPOT)。根据路径的在线时间期望获得节点的插入位置,节点中断后利用组播节点在线时间的重尾现象,在恢复被迫中断节点时保留节点在线时间信息,构建高稳定性组播树。同时针对断裂恢复问题,提出带一阶预测的MPOT算法。模拟结果表明,2种算法构建的组播树均具有较高的稳定性。     

一种利用双路径组播树提高应用层组播稳定性的方案

应用层组播作为IP组播的替代方案,具有简单灵活、容易部署的优点,在分布式多媒体通信领域获得了广泛的应用.但其存在组播树稳定性的问题,发展受到了限制.该文提出一种双路径组播树方案,采用两个组播树传输和转发不同内容的数据,在节点加入或者退出组播组时,不需要重新构建组播树,其它节点仍然可以接收到可接受的多媒体数据,从而减少故障恢复的时间,提高应用层组播的稳定性,改善应用层组播的性能.最后通过计算机仿真,表明该方案在节点加入和退出时减少了故障恢复时间,维护了组播树的稳定性,具有良好的性能改善和显著的优点,适合多媒体业务传输.     

一种基于策略函数的应用层组播路由算法

由于IP组播存在可扩展性差、难以管理等方面的缺陷,研究人员提出了应用层组播.实时传输是应用层组播技术的一个主要应用领域,对网络延迟有严格的限制.文中着重研究构建最小延迟应用层组播树的算法,提出一种基于策略函数构造应用层最小直径组播树的启发式算法BCT-H.该算法采用策略函数迭代的选择使生成树直径最短的路径,从而有效地减少了网络中的转发时延和同一条链路的重复分组数量.模拟实验表明该算法能够有效地降低链路强度,减少组播树的时延.     

基于节点异构性的应用层组播算法

为降低组播树的维护开销、改善组播树的负载平衡、提高组播树的性能,给出一种基于节点异构性的应用层组播算法。与已有的应用层组播算法相比,综合考虑了节点的异构性(动态计算节点异构性,综合考虑节点的网络转发能力和计算能力来),提出了全新的组播树构建策略并通过黄牌节点的数量来及时调整组播树。仿真结果表明,该算法大大降低了端到端的延迟和平均链路伸展长度,提高了系统的稳定性。     

一种基于策略函数的应用层组播路由算法

由于IP组播存在可扩展性差、难以管理等方面的缺陷,研究人员提出了应用层组播。实时传输是应用层组播技术的一个主要应用领域,对网络延迟有严格的限制。文中着重研究构建最小延迟应用层组播树的算法,提出一种基于策略函数构造应用层最小直径组播树的启发式算法BCT-H。该算法采用策略函数迭代的选择使生成树直径最短的路径,从而有效地减少了网络中的转发时延和同一条链路的重复分组数量。模拟实验表明该算法能够有效地降低链路强度,减少组播树的时延。     

Input-to-state stable finite horizon MPC for neutrally stable linear discrete-time systems with input constraints

MPC or model predictive control is representative of control methods which are able to handle inequality constraints. Closed-loop stability can therefore be ensured only locally in the presence of constraints of this type. However, if the system is neutrally stable, and if the constraints are imposed only on the input, global asymptotic stability can be obtained; until recently, use of infinite horizons was thought to be inevitable in this case. A globally stabilizing finite-horizon MPC has lately been suggested for neutrally stable continuous-time systems using a non-quadratic terminal cost which consists of cubic as well as quadratic functions of the state. The idea originates from the so-called small gain control, where the global stability is proven using a non-quadratic Lyapunov function. The newly developed finite-horizon MPC employs the same form of Lyapunov function as the terminal cost, thereby leading to global asymptotic stability. A discrete-time version of this finite-horizon MPC is presented here. Furthermore, it is proved that the closed-loop system resulting from the proposed MPC is ISS (Input-to-State Stable), provided that the external disturbance is sufficiently small. The proposed MPC algorithm is also coded using an SQP (Sequential Quadratic Programming) algorithm, and simulation results are given to show the effectiveness of the method.     

Robust ISS of uncertain discrete-time singularly perturbed systems with disturbances

This paper is concerned with robustly input-to-state stable (ISS) and Robust ISS by feedback of uncertain discrete-time singularly perturbed systems (SPSs) with disturbances. Meanwhile, robust stability and stabilisation of uncertain discrete-time SPSs are also obtained as the particular cases of robust ISS and robust ISS by feedback. We first find a sufficient condition by using the fixed-point principle in terms of linear matrix inequalities (LMIs) to guarantee that the considered system is always standard discrete-time SPSs subject to uncertainty and disturbances. Then, the full systems could decompose into the continuous-time uncertain slow subsystem with disturbance and discrete-time uncertain fast subsystems with disturbance, respectively. Based on the two-time-scale decomposition technique, sufficient condition in terms of LMIs is given such that the full systems are uniformly standard and robust ISS simultaneously. In addition, a state feedback controller is constructed by using the LMI approach such that the resulting closed-loop systems are robust ISS. Finally, a numerical example is provided to illustrate the effectiveness of the proposed approach.     

一种基于神经网络的数据驱动参数整定法

针对难以建立较准确数学模型的非线性被控对象,提出了一种基于神经网络的数据驱动控制器参数整定法.其设计思想是结合虚拟目标值和神经网络,跳过被控对象,直接得到控制器.此外,利用李亚普诺夫理论证明了神经网络的学习速率在一定范围内可以保证控制器的跟踪误差收敛,并且利用虚拟参考反馈整定(VRFT)算法中的滤波器,结合泰勒展开式,进一步验证了闭环控制系统的稳定性.仿真表明,该方法具有计算负担小,采用数据量少,调节参数方便,强跟踪性等优点.     

自适应动态规划综述

自适应动态规划(Adaptive dynamic programming, ADP)是最优控制领域新兴起的一种近似最优方法, 是当前国际最优化领域的研究热点. ADP方法 利用函数近似结构来近似哈密顿--雅可比--贝尔曼(Hamilton-Jacobi-Bellman, HJB)方程的解, 采用离线迭代或者在线更新的方法, 来获得系统的近似最优控制策略, 从而能够有效地解决非线性系统的优化控制问题. 本文按照ADP的结构变化、算法的发展和应用三个方面介绍ADP方法. 对目前ADP方法的研究成果加以总结, 并对这 一研究领域仍需解决的问题和未来的发展方向作了进一步的展望.     

Stability analysis for a class of random nonlinear impulsive systems

In this paper, the problem of noise‐to‐state stability (NSS) and globally asymptotic stability (GAS) is investigated for a class of nonlinear systems with random disturbances and impulses, where the random noises have finite second‐order moments and the so‐called random impulses mean that impulse ranges are driven by a sequence of random variables. First, some general conditions are given to guarantee the existence and uniqueness of solutions to random nonlinear impulsive systems. Next, when the continuous dynamics are stable but the impulses are destabilizing, the NSS and GAS of random nonlinear impulsive systems are examined by the average impulsive interval approach. Then, when the continuous dynamics are unstable but the impulses are stabilizing, it is shown that the NSS and GAS can be retained by using the reverse average impulsive interval approach. Finally, the theoretical findings are substantiated with illustrative examples. Copyright © 2016 John Wiley & Sons, Ltd.     

线性自抗扰控制器的稳定性研究

研究了线性扩张状态观测器(Extended state observer, ESO)的估计能力,并且分析了在线性自抗扰控制(Linear active disturbance rejection control, LADRC)下闭环系统的稳定性. 对于系统模型未知的情形, 给出了线性扩张观测器估计误差有界的证明, 并通过分析得出了如下结论: 在扩张状态观测器跟踪误差趋于零的前提下, 在线性自抗扰控制下的闭环系统可以实现对设定信号的精确跟踪以及输入-输出有界(Bounded input and bounded output, BIBO)稳定.     

一种基于跟踪微分器的简单自适应控制算法

应用跟踪微分器在处理信号时具有较强的误差衰减能力和抗扰动能力,针对传统的简单自适应控制算法中增益调节过于简单、收敛速度慢且适应性不强的缺点,对传统的简单自适应算法进行了改进,提高了收敛速度,减小了静差。描述了该算法的结构和原理,运用Lyapunov稳定性理论和LaSalle不变性原理证明了控制算法是稳定的,跟踪误差收敛到零。计算机仿真结果验证了算法的可行性和有效性,并给出了参数选择的一般法则。     

典型工业过程的无超调预测控制设计

讨论了二阶系统具有无超调响应的充分必要条件,并结合一类典型工业对象的预测控制设计,分析了相应设计参数对闭环系统的影响,得到了具有无超调控制特性的条件。仿真结果表明,所得出的结论是正确的。     

LMI stability criterion with less variables for time-delay systems

Slack variables approach is an important technique for tackling the delay-dependent stability problem for systems with time-varying delay. In this paper, a new delay-dependent stability criterion is presented without introducing any slack variable. The technique is based on a simply integral inequality. The result is shown to be equivalent to some existing ones but includes the least number of variables. Thus, redundant selection and computation can be avoided so that the computational burden can be largely reduced. Numerical examples are given to illustrate the effectiveness of the proposed stability conditions. Recommended by Editorial Board member Young Soo Suh under the direction of Editor Jae Weon Choi. The authors would like to thank the Associate Editor and the Reviewers for their very helpful comments and suggestions. This work was supported in part by the Funds for Creative Research Groups of China under Grant 60821063, by the State Key Program of National Natural Science of China under Grant 60534010, by the Funds of National Science of China under Grant 60674021, 60774013, 60774047, National 973 Program of China under Grant No. 2009CB320604, and by the Funds of Ph.D. program of MOE, China under Grant 20060145019 and the 111 Project B08015. Xun-Lin Zhu received the B.S. degree in Applied Mathematics from Information Engineering Institute, Zhengzhou, China, in 1986, the M.S. degree in basic mathematics from Zhengzhou University, Zhengzhou, China, in 1989, and the Ph.D. degree in Control Theory and Engineer-ing from Northeastern University, Shenyang, China, in 2008. Currently, he is an Associate Professor at Zhengzhou University of Light Industry, Zhengzhou, China. His research interests include neural networks and networked control systems. Guang-Hong Yang received the B.S. and M.S. degrees in Northeast University of Technology, China, in 1983 and 1986, respectively, and the Ph.D. degree in Control Engineering from Northeastern University, China (formerly, Northeast University of Technology), in 1994. He was a Lecturer/Associate Professor with Northeastern University from 1986 to 1995. He joined the Nanyang Technological University in 1996 as a Postdoctoral Fellow. From 2001 to 2005, he was a Research Scientist/Senior Research Scientist with the National University of Singapore. He is currently a Professor at the College of Information Science and Engineering, Northeastern University. His current research interests include fault-tolerant control, fault detection and isolation, non-fragile control systems design, and robust control. Dr. Yang is an Associate Editor for the International Journal of Control, Automation, and Systems (IJCAS), and an Associate Editor of the Conference Editorial Board of the IEEE Control Systems Society. Tao Li was born in 1979. He is now pursuing a Ph.D. degree in Research Institute of Automation Southeast University, China. His current research interests include time-delay systems, neural networks, robust control, fault detection and diagnosis. Chong Lin received the B.Sci and M.Sci in Applied Mathematics from the Northeastern University, China, in 1989 and 1992, respectively, and the Ph.D in Electrical and Electronic Engineering from the Nanyang Technological University, Singapore, in 1999. He was a Research Associate with the University of Hong Kong in 1999. From 2000 to 2006, he was a Research Fellow with the National University of Singapore. He is currently a Profesor with the Institute of Complexity Science, Qingdao University, China. His current research interests are mainly in the area of systems analysis and control. Lei Guo was born in 1966. He received the Ph.D. degree in Control Engineering from Southeast University (SEU), PR China, in 1997. From 1999 to 2004, he has worked at Hong Kong University, IRCCyN (France), Glasgow University, Loughborough University and UMIST, UK. Now he is a Professor in School of Instrument Science and Opto-Electronics Engineering, Beihang University. He also holds a Visiting Professor position in the University of Manchester, UK and an invitation fellowship in Okayama University, Japan. His research interests include robust control, stochastic systems, fault detection, filter design, and nonlinear control with their applications.     

Distributed Control of Nonlinear Uncertain Systems: A Cyclic-small-gain Approach

This paper presents a cyclic-small-gain approach to distributed control of nonlinear multi-agent systems for output agreement. Through a novel nonlinear control law design, the output agreement problem is transformed into a stabilization problem, and the closed-loop multi-agent system is transformed into a large-scale system composed of input-to-state stability (ISS) subsystems which are interconnected with each other through redefined outputs. By forcing the redefined outputs to go to arbitrarily small neighborhoods of the origin, practical consensus is achieved for the agents in the sense that their outputs ultimately converge to each other within an arbitrarily small region. A recently developed cyclic-small-gain result is adopted to assign appropriately the ISS gains to the transformed interconnected system. Moreover, if the system is disturbancefree, then consensus can be guaranteed. Interestingly, the closedloop multi-agent system is also robust to bounded time-delays and disturbances in information exchange.