Stability analysis and output‐feedback stabilization of discrete‐time systems with a time‐varying state delay and nonlinear perturbation

This paper aims to derive stability conditions and an output‐feedback stabilization method for discrete‐time systems with a time‐varying state delay and nonlinear perturbation. With a new way of handling the Lyapunov stability criterion, linear matrix inequality conditions are obtained for estimating bounds on delay to ensure the asymptotic stability. Based on the conditions, a synthesis procedure is developed for finding stabilizing output‐feedback gains, which are formulated as direct design variables. Three numerical examples are employed to demonstrate the effectiveness and advantages of the proposed method. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

面向主体脆弱性的权限控制安全分析模型

在网络攻击过程中脆弱性存在于节点的部件主体上,针对该问题,将攻击描述细化到网络部件级,在原权限控制模型中增加对部件间权限、连接关系、属性的描述及脆弱性重写规则,构造NCVTG模型,提出复杂度为多项式时间的NCVTG模型图权限转移闭包生成算法用以评估网络的动态变化,给出当前脆弱性状态下的所有攻击路径。实验结果证明,该模型可对网络安全性进行综合分析,预测所有可能的攻击。     

改进的TCP/RED模型的稳定性分析

TCP自同步特性可提高网络稳定性,TCP/RED拥塞控制模型忽略了TCP自同步特性。针对该问题,提出包含TCP自同步特性更接近实际网络的改进TCP/RED拥塞控制模型。应用Nyquist稳定性判据给出当改进模型在复平面上属于某一不包含-1+j0点的下半平面时的稳定性条件,得到比TCP/RED模型更宽松的RED参数设置范围。NS2仿真验证了该稳定性条件的有效性。     

基于邻居节点表更新的可变度分群算法

对移动Ad Hoe网络而言,当网络规模较大时一般采用分级结构来提高网络的可扩展性,如何合理地保障分群是移动AdHoe网络实现分群网络并进行分群路由的前提和核心环节.针对这个关键问题,在分析节点链路改变次数随节点平均移动速度变化的基础上,提出了一种基于邻居节点表更新的可变度分群算法.仿真分析表明,该算法生成的群在稳定性和负载平衡性方面均要优于经典的分群算法.     

包含SVC和非线性负荷的电力系统耗散实现与控制

采用Hamilton函数方法研究了包含静止无功补偿器(SVC)和非线性负荷的电力系统的反馈控制问题. 首先建立了系统的非线性微分代数方程模型, 通过预置状态反馈完成了耗散Hamilton实现. 然后利用该耗散实现结构,通过阻尼注入方式设计了基于能量的SVC非线性控制器. 本文所设计的控制器结构简单, 物理意义明确. 仿真结果表明该控制器能有效提高电力系统的暂态稳定性.     

球形对象族的最优鲁棒镇定

本文对球形对象族系统最优鲁棒镇定问题进行了研究.利用最小范数解方法求解球形对象族的可镇定性半径.可镇定性半径是系统稳定性半径的上界,最优控制器的稳定性半径等于镇定性半径.文中给出球形对象族最优鲁棒控制器的形式,并通过示例具体说明球形对象族最优鲁棒控制器的设计方法.     

含区间时变时滞的线性不确定系统鲁棒稳定性新判据

研究一类区间时变时滞线性不确定系统的鲁棒稳定性问题.通过引入增广Lyapunov泛函,结合积分不等式方法,导出了区间时变时滞线性系统的时滞相关鲁棒稳定性新判据.与现有方法不同,该方法不涉及自由权矩阵技术和任何模型变换,减少了理论和计算上的复杂性,而且在估计Lyapunov泛函导数的上界时没有忽略某些有用项.数值算例表明,所提出的判据是有效的,具有更低的保守性.     

Bayesian inference and prediction of the inverse Weibull distribution for Type-II censored data

This paper describes the Bayesian inference and prediction of the inverse Weibull distribution for Type-II censored data. First we consider the Bayesian inference of the unknown parameter under a squared error loss function. Although we have discussed mainly the squared error loss function, any other loss function can easily be considered. A Gibbs sampling procedure is used to draw Markov Chain Monte Carlo (MCMC) samples, and they have in turn, been used to compute the Bayes estimates and also to construct the corresponding credible intervals with the help of an importance sampling technique. We have performed a simulation study in order to compare the proposed Bayes estimators with the maximum likelihood estimators. We further consider one-sample and two-sample Bayes prediction problems based on the observed sample and provide appropriate predictive intervals with a given coverage probability. A real life data set is used to illustrate the results derived. Some open problems are indicated for further research.     

减少离散大系统两级镇定控制的保守性

The aim of this paper is to propose a new algorithm for multilevel stabilization of large scale systems. In two-level stabilization method, a set of local stabilizers for the individual subsystems in a completely decentralized environment is designed. The solution of the control problem involves designing of a global controller on a higher hierarchical level that provides corrective signals to account for interconnections effect. The principle feature of this paper is to reduce conservativeness in global controller design. Here, the key point is to reduce the effect of interactions instead of neutralizing them. In fact, unlike prior methods, our idea does not ignore the possible beneficial aspects of the interactions and does not try to neutralize them.     

基于机器人群的主动传感器网络自组织的运动规划

主动传感器网络的自组织通常要求移动节点群(机器人群)通过障碍物环境移动到指定地点后, 重新调整并按预定布局组网. 在网络的自组织过程中要保证每个移动节点(机器人)与整个网络之间的连通性. 在对移动机器人的保持连通性进行优化的基础上, 提出了单步位置预测与群体势场相结合的分布式运动规划方法进行主动传感器网络的部署和重置, 证明了机器人运动控制的稳定性和网络的连通性保持, 进行了有和无障碍物环境下超过40个机器人的仿真, 结果表明该方法适用于大规模的主动传感器网络重置, 并对不同规模的网络具有可扩展性.