Dynamic Output Feedback Consensualization of Uncertain Swarm Systems with Time Delays

Consensus analysis and design problems of high‐dimensional discrete‐time swarm systems in directed networks with time delays and uncertainties are dealt with by using output information. Two subspaces are introduced, namely a consensus subspace and a complement consensus subspace. By projecting the state of a swarm system onto the two subspaces, a necessary and sufficient condition for consensus is presented, and based on different influences of time delays and uncertainties, an explicit expression of the consensus function is given which is very important in applications of swarm systems. A method to determine gain matrices of consensus protocols is proposed. Numerical simulations are presented to demonstrate theoretical results.     

一类不确定线性系统圆盘极点鲁棒可靠配置

针对一类不确定线性系统,基于线性矩阵不等式（LMI）,给出状态反馈鲁棒控制器的设计算法。利用比离散故障模型更具有实际意义的连续故障模型,给出确定状态反馈鲁棒可靠控制器存在的充分条件,通过求解LMI,确定鲁棒可靠控制器的参数矩阵。所给出的鲁棒可靠控制器,不仅可以保持不确定闭环系统的极点保持在圆盘内,而且能够抵御执行器故障对圆盘极点的影响。一个数值例子说明所给出方法的有效性及可行性。     

CDDL:动态描述逻辑的不确定性扩展

本体(Ontology)是语义Web中共享知识的形式化建模工具,其逻辑基础是描述逻辑.动态描述逻辑(DDL)具有同时表示静态和动态知识的优势.本文针对语义Web需要处理不确定性动态知识的需求,利用云模型对DDL进行不确定性扩展,提出了一种能够有效实现不确定性静态和动态知识进行表示和推理的不确定性动态描述逻辑CDDL.与...     

基于Markov理论的改进灰色GM(1,1)预测模型研究

在灰色预测的基础上,引入马尔可夫链预测理论,建立了灰色马尔可夫预测模型.它是将灰色预测模型与马尔可夫预测方法优化组合,用灰色预测模型预测随机时间序列数据的总体发展趋势,而用马尔可夫链模型预测各数据在总体趋势下的随机波动性变化,得到随机时间序列趋势预测模型的解.通过公路运输实际数据进行了验证,结果表明:灰色马尔可夫预测模...     

基于云模型的图像区域分割方法

基于区域的图像分割方法由于其高效、稳健的特点成为自动或半自动图像分割方法的研究热点之一。针对区域分割方法中存在的不确定性问题,提出了一种基于云模型的区域分割方法。首先以云变换为基础确定了区域生长过程中的生长准则,然后以逆向云算法实现分割区域由定量的像素集合到定性的云概念的转换过程,最后以云综合算法为基础将相邻区域进行合并,实现了基于区域的不确定性图像分割。两组图像分割实验表明该方法可以准确地分割出目标,并优于传统的图像分割算法。     

Limit load and shakedown analysis of plastic structures under stochastic uncertainty

Problems from plastic analysis are based on the convex, linear or linearised yield/strength condition and the linear equilibrium equation for the stress (state) vector. In practice one has to take into account stochastic variations of several model parameters. Hence, in order to get robust maximum load factors, the structural analysis problem with random parameters must be replaced by an appropriate deterministic substitute problem. A direct approach is proposed based on the primary costs for missing carrying capacity and the recourse costs (e.g. costs for repair, compensation for weakness within the structure, damage, failure, etc.). Based on the mechanical survival conditions of plasticity theory, a quadratic error/loss criterion is developed. The minimum recourse costs can be determined then by solving an optimisation problem having a quadratic objective function and linear constraints. For each vector a(·) of model parameters and each design vector x, one obtains then an explicit representation of the “best” internal load distribution F^∗. Moreover, also the expected recourse costs can be determined explicitly. Consequently, an explicit stochastic nonlinear program results for finding a robust maximal load factor μ^∗. The analytical properties and possible solution procedures are discussed.     

路由器配置策略智能提取技术

为实现配置故障智能管理目标,提高路由器配置管理效率,提出对路由器配置文本按功能分段的算法,研究提取各分段配置策略的推理技术,构建基于可信度方法提取各分段配置策略的推理机模型。试验结果表明,它们均有94%以上的分段正确率和策略提取准确率,为构建配置故障智能管理系统做好了技术准备。     

Sampled-data based average consensus with measurement noises: convergence analysis and uncertainty principle

In this paper, sampled-data based average-consensus control is considered for networks consisting of continuous-time first-order integrator agents in a noisy distributed communication environment. The impact of the sampling size and the number of network nodes on the system performances is analyzed. The control input of each agent can only use information measured at the sampling instants from its neighborhood rather than the complete continuous process, and the measurements of its neighbors’ states are corrupted by random noises. By probability limit theory and the property of graph Laplacian matrix, it is shown that for a connected network, the static mean square error between the individual state and the average of the initial states of all agents can be made arbitrarily small, provided the sampling size is sufficiently small. Furthermore, by properly choosing the consensus gains, almost sure consensus can be achieved. It is worth pointing out that an uncertainty principle of Gaussian networks is obtained, which implies that in the case of white Gaussian noises, no matter what the sampling size is, the product of the steady-state and transient performance indices is always equal to or larger than a constant depending on the noise intensity, network topology and the number of network nodes.     

一类多通道不确定时滞大系统分散鲁棒H∞控制：LMI方法

研究多通道不确定时滞大系统的鲁棒分散H_∞控制问题. 假定不确定性是时不变、范数有界, 且存在于系统、时滞和输出矩阵中. 主要针对动态输出反馈控制问题. 基于Lyapunov稳定性理论, 通过设定Lyapunov矩阵为合适的块对角结构, 采用矩阵替换的方法推导出了使多通道不确定时滞大系统可鲁棒镇定, 且满足一定的扰动水平的时滞依赖充分条件即线性矩阵不等式(LMI) 有可行解, 并且给出了具有期望阶数的分散鲁棒控制器的设计方法. 数值例子说明了本文提出方法的有效性.     

时变不确定多时滞连续系统的最优保成本控制

针对一类同时具有状态多时滞和输入多时滞的时变不确定连续多时滞系统。研究保成本状态反馈控制器的设计。假定其中的时变不确定性项是范数有界的,但不需要满足匹配条件,通过构造改造的Lyapunov函数和线性矩阵不等式（LMI）方法,给出系统满足保性能指标的一个充分条件,仅通过求解一个相应的线性矩阵不等式,就可得到保性能控制器使得闭环系统的一个保成本函数对所有允许的不确定参数有上界。通过求解凸优化问题得到最优保性能控制器,最后用数值例子说明该方法的有效性。