通信受限的多智能体系统二分实用一致性

针对存在量化数据、通信时滞等通信约束以及带有竞争关系的多智能体系统, 研究其二分实用一致性问题, 提出了一种基于量化器的分布式控制协议. 该协议基于结构平衡拓扑假设, 通过规范变换将具有竞争关系系统转变为具有非负连接权重系统, 使二分实用一致性问题转变为一般实用一致性问题. 利用微分包含理论、菲利波夫解的框架、代数图论以及Lyapunov稳定性理论, 证明了在本文所提控制策略下, 具有竞争关系的多智能体系统能实现二分实用一致, 即智能体状态收敛至模相同但符号不同的可控区间, 并给出了误差收敛上界值. 仿真试验进一步验证了理论结果的有效性.     

对抗网络下时滞非线性多智能体系统固定时间二分一致性

本文针对一类存在输入时延的非线性多智能体系统,研究了其在结构平衡的无向符号图下的固定时间二分一致性问题.首先,本文针对智能体间相互合作与相互竞争的关系,设计了一类存在输入时延的多智能体系统固定时间分布式一致性控制协议,使得系统状态在固定时间内收敛到数值相同但符号相反的两个值,且收敛时间上界与初始状态无关.随后,利用Lyapunov稳定性理论和代数图论给出了在存在输入时延的情况下多智能体系统实现固定时间二分一致性的充分条件和收敛时间的上界值,证明了控制算法的稳定性.最后,仿真实例验证了所提固定时间二分一致性算法和理论结果的有效性.     

符号网络的局部标注特征与预测方法

当复杂网络的边具有正、负属性时称为符号网络。符号为正表示两用户间具有相互信任（朋友）关系,相反,符号为负表示不信任（敌对）关系。符号网络中的一个重要研究任务是给定部分观测的符号网络,预测未知符号。分析发现,具有弱结构平衡特征的符号网络,其邻接矩阵呈现全局低秩性,在该特征下链路符号预测问题可以近似表达为低秩矩阵分解问题。但基本低秩模型中,相邻节点间符号标注的局部行为特征未得到充分利用,论文提出了一种带偏置的低秩矩阵分解模型,将邻居节点的出边和入边符号特征作为偏置信息引入模型,以提高符号预测的精度。利用真实符号网络数据进行的实验证明,所提模型能够获得较其他基准算法好的预测效果且算法效率高。     

符号网络下一类非线性二分一致性系统的跟踪控制

研究具有对抗交互的一类非线性多智能体系统的二分一致性跟踪控制问题,借助有向符号图刻画智能体之间的通信关系,并假定符号图是结构平衡的.进一步,借助符号图网络对二分一致性跟踪问题进行公式化描述.基于最近邻原则,结合拉普拉斯矩阵、牵制矩阵、符号函数、耦合增益参数等,设计分布式控制律.利用Barbalat引理和李亚谱诺夫函数证明整个闭环系统的渐近稳定性,在保持闭环系统稳定的条件下推导出耦合增益的下界.最后通过仿真验证所提出方法的有效性.     

基于改进布谷鸟搜索算法的LEACH协议

LEACH协议是一种低能耗自适应的无线传感器网络聚类层次算法。它存在着随机选取簇头、不考虑节点剩余能量和节点位置等缺点。针对这些问题,本文提出一种基于改进布谷鸟算法的LEACH协议。布谷鸟算法（CS）是一种新型的智能优化算法。为了提高算法的局部搜索能力,对标准的布谷鸟算法进行改进：1）引入权重系数,调整算法收敛速度;2）所有个体从优到差进行排序,排序后将种群分为2部分,这2部分个体选择不同的飞行方式,避免较优个体陷入局部最优。改进后的协议将簇头选择过程分为临时簇头优化和形式簇头选择。首先,利用传统LEACH协议生成临时簇头,然后基于MCS对簇头进行优化,根据节点剩余能量选择形式化簇头。实验结果表明,与LEACH相比,新算法能有效平衡网络负载,提高能源的利用率,延长网络的生命周期。     

云粒子群优化算法在无线传感器网络中的应用

无线传感器网络中节点计算能力和存储存能量有限的问题一直制约着无线传感器网络的发展.为此,本文提出了一种基于云PSO（particle swarm optimization）算法的无线传感器网络能量优化方法,主要包括网络分簇、网络能量模型建立、云PSO算法迭代优化等步骤.其中云PSO算法采用云理论模型优选惯性权重可以提高PSO算法的收敛速度,典型函数测试结果表明其效果优于常规PSO算法和遗传算法;在网络建模中采用二分功率控制算法可以降低网络能耗、延长节点寿命.最后经仿真试验和对比分析表明本文提出的方法在优化无线传感器网络中具有速度快、节点生存能力强的优点,并能有效地控制网络能耗.     

基于遗传模拟退火算法的无线传感器网路由协议

在无线传感器网络中(WSNs)中,由于节点能量有限,为了延长整个网络的生存周期,提出一种基于遗传模拟退火算法的无线传感器网络路由协议.利用模拟退火(SA)算法具有较强的局部搜索能力并能以稳定的速度收敛,克服遗传算法(GA)局部搜索能力差并容易早熟收敛等缺点.该路由协议在簇头节点选举时充分考虑了节点的剩余能量,并根据网络中数据转发能量耗损和延迟时间建立个体适应度函数,采用遗传模拟退火算法找到簇头节点到基站的最优路径.仿真结果表明:与其他协议比较,该方法不仅可以均衡各个节点的剩余能量,还可以有效延长整个网络生存周期和提高网络的数据传输能力.     

d-子树划分问题

边赋非负权无向简单图的一簇顶点两两不相交的子树称为它的一个d-子树划分(d为非负实数),如果这些子树的顶点集的并等于此图的顶点集,且每棵子树的直径不超过d.图的d-子树划分问题就是求它的一个含子树数最少的d-子树划分及其所含的子树数.d-子树划分问题在有线通信网络、道路交通网络、城市供水网络、电力传输网络等网络的运行管理、维护与测试中具有很强的应用背景.文中证明了对任意正实数d,边赋非负权二分平面图的d-子树划分问题是NP-完全问题;提出了求解边赋非负权树d-子树划分问题的一个线性时间算法,详细地讨论了算法的实现策略.所提出的算法具有简明易实现、耗费时间少等特点.     

一种基于水下无线传感器网络的目标跟踪方法

本发明公开了一种基于水下无线传感器网络的目标跟踪方法。该方法首先根据最强信号原则选择簇节点,然后根据单跳距离准则组成簇网络对目标进行观测,如果观测信号强度超过阈值,则发送观测数据给簇头节点。簇头节点接收到粗内节点传送的数据,采用改进重采样的粒子滤波算法对当前时刻的目标位置和方差进行估计。根据目标的运动不断地更新簇头节点,将上一簇头节点状态估计值和方差估计值传送给当前簇头节点,再由当前簇头节点采用改进的重采样粒子滤波算法估计运动目标位置,直到运动目标超出了水下无线传感器网络的跟踪范围;本发明使用改进重采样算法的粒子滤波跟踪方法估计水下目标的位置和方差,提高水下无线传感器网络的目标跟踪性能。     

基于有向网络的一致性跟踪算法

研究了leader有控制输入且followers未知该输入条件下的线性多智能 体一致性跟踪问题.提出两种一致性跟踪算法,证明两种算法在leader到followers存在一棵 有向生成树且follower间拓扑是有向条件下,网络就能跟踪leader的状态.对于第一种算法,节点根 据相邻节点或leader的状态来求解其控制输入,并基于代数Riccati不等式给出 连续情形下算法稳定性条件.第二种算法直接利用相邻节点或leader的状态,使followers在上述网络条件下跟踪leader的状态,同样基于代数Riccati不等式给出算法稳定性条件. 仿真结果验证了算法的有效性.     

Bipartite Byzantine-resilient event-triggered consensus control of heterogeneous multi-agent systems

This article studies the bipartite resilient event-triggered consensus control for a class of the heterogeneous multi-agent systems. Due to the external cyberattacks, some agents may become the Byzantine agents and will affect the behavior of the other agents. To improve the security of the multi-agent systems against the Byzantine agents, a novel bipartite event-triggered heterogeneous mean-subsequence-reduced algorithm is designed. First, to handle the heterogeneous multi-agent systems, a state transformation is carefully designed, to facilitate the design and analysis of the bipartite resilient consensus algorithm. Based upon the designed state transformation, the bipartite resilient control inputs are constructed, where the structural balance analysis shows that the resulting effective signed graph and the equivalent signed graph are both structurally balanced, if the signed graph of the multi-agent systems is structurally balanced. In addition, a dynamic event-triggered mechanism is proposed, where a set of dynamic factors are introduced into the event-triggered functions to prevent the usage of the global topology information. By virtue of the designed algorithm, it is guaranteed that the heterogeneous multi-agent systems can achieve the bipartite consensus in the presence of the Byzantine agents, and the communication burden among the agents can be reduced. The numerical simulations are conducted to verify the effectiveness of the proposed algorithm.     

Signed consensus problems on networks of agents with fixed and switching topologies

This paper deals with signed consensus problems for networks of agents in the presence of both fixed and switching topologies. By specifying signs for the agents, they are divided into two groups and are enabled to reach agreement on a consensus value which is the same in modulus for both groups but not in sign. Using nearest-neighbour interaction rules, we propose the distributed protocols and address their exponential convergence problem. It is shown that the quasi-strong connectivity of fixed networks or joint quasi-strong connectivity of switching networks can provide a necessary and sufficient guarantee for all agents to achieve signed consensus exponentially fast. In particular, the signed consensus results can include as special cases those of bipartite consensus in signed networks with fixed and switching topologies. Numerical simulations are also provided to illustrate the exponential convergence performance of the proposed signed consensus protocols.     

多智能体系统基于自适应控制下的二分-分量一致性

二分-分量一致性是指网络系统中部分具有相似特性智能体的某些分量随时间推移趋于相同的值,而剩余智能体的某些分量则随时间趋于相反的值,是一种弱于恒同一致和二分一致的动力学行为.为此,对符号网络上非线性多智能体系统的二分-分量一致性问题展开研究.首先,针对多智能体系统中各智能体之间存在的合作关系或竞争关系,设计有效的自适应牵制控制器;随后,基于Lyapunov稳定性理论和矩阵理论,导出该非线性系统二分-分量一致性得以实现的充分条件;最后,通过数值模拟验证理论结果.     

On voting-based consensus of cluster ensembles

Voting-based consensus clustering refers to a distinct class of consensus methods in which the cluster label mismatch problem is explicitly addressed. The voting problem is defined as the problem of finding the optimal relabeling of a given partition with respect to a reference partition. It is commonly formulated as a weighted bipartite matching problem. In this paper, we present a more general formulation of the voting problem as a regression problem with multiple-response and multiple-input variables. We show that a recently introduced cumulative voting scheme is a special case corresponding to a linear regression method. We use a randomized ensemble generation technique, where an overproduced number of clusters is randomly selected for each ensemble partition. We apply an information theoretic algorithm for extracting the consensus clustering from the aggregated ensemble representation and for estimating the number of clusters. We apply it in conjunction with bipartite matching and cumulative voting. We present empirical evidence showing substantial improvements in clustering accuracy, stability, and estimation of the true number of clusters based on cumulative voting. The improvements are achieved in comparison to consensus algorithms based on bipartite matching, which perform very poorly with the chosen ensemble generation technique, and also to other recent consensus algorithms.     

Distributed Control for Signed Networks of Nonlinear Agents

This paper copes with distributed control problems for signed networks that consist of a group of nonlinear agents. A distributed control algorithm is designed by using the nearest neighbor rule. For Lipschitz-type nonlinear dynamics, this algorithm guarantees structurally balanced signed networks to achieve bipartite consensus and structurally unbalanced signed networks to reach state stability, respectively. When bounded nonlinear dynamics are considered, all agents exponentially converge to a definite bound within a finite time, regardless of whether the signed networks are structurally balanced or structurally unbalanced. A Lyapunov approach is simultaneously exploited to carry out the dynamic behaviors analysis of signed networks. Four examples are provided to demonstrate the validity of the developed theoretical results.     

Adaptive bipartite consensus control of high‐order multiagent systems on coopetition networks

In this paper, a bipartite consensus problem is considered for a high‐order multiagent system with cooperative‐competitive interactions and unknown time‐varying disturbances. A signed graph is used to describe the interaction network associated with the multiagent system. The unknown disturbances are expressed by linearly parameterized models, and distributed adaptive laws are designed to estimate the unknown parameters in the models. For the case that there is no exogenous reference system, a fully distributed adaptive control law is proposed to ensure that all the agents reach a bipartite consensus. For the other case that there exists an exogenous reference system, another fully distributed adaptive control law is also developed to ensure that all the agents achieve bipartite consensus on the state of the exogenous system. The stability of the closed‐loop multiagent systems with the 2 proposed adaptive control laws are analyzed under an assumption that the interaction network is structurally balanced. Moreover, the convergence of the parameter estimation errors is guaranteed with a persistent excitation condition. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed adaptive bipartite consensus control laws for the concerned multiagent system.     

Finite-time weighted average consensus with respect to a monotonic function and its application

The consensus state is an important and fundamental quantity for consensus problems of multi-agent systems, which indicates where all the dynamical agents reach. In this paper, weighted average consensus with respect to a monotonic function, which means that the trajectories of the monotonic function along the state of each agent reach the weighted average of their initial values, is studied for a group of kinematic agents with time-varying topology. By constructing a continuous nonlinear distributed protocol, such a consensus problem can be solved in finite time even though the time-varying topology involves unconnected graphs. Then the distributed protocol is employed to compute the maximum-likelihood estimation of unknown parameters over sensor networks. Compared with the existing results, the estimate scheme proposed here may reduce the costs of data communication, storage memory, book-keeping and computational overheads.     

异质多智能体系统二分一致性的充要条件

针对由一阶智能体和二阶智能体组成的异质多智能体系统的二分一致性问题,对连续和离散系统情形分别设计了二分一致性协议。基于结构平衡的拓扑,通过规范变换实现了从具有敌对关系的系统到具有非负连接权重系统的转化,将二分一致性问题转变为一般一致性问题。进一步,运用代数图论和矩阵理论分析闭环控制系统的动态特性,得到了异质多智能体系统渐近实现二分一致性的充要条件。最后通过数值模拟验证了所得结果的有效性。     

Event‐triggered Control for Cluster Consensus in Multi‐agent Networks

This paper considers the issue of cluster consensus for multiple agents in fixed and undirected networks. Agents in a network are supposed to split into several clusters, and a fraction of the agents in each cluster are pinned by virtual leaders. According to the Lyapunov stability theory and graph theory, some appropriate event‐triggered protocols are developed for consensus of the agents belonging to the same cluster, which can greatly reduce both the number of communication updates and that of control actuation updates. Finally, a numerical example is shown to demonstrate the effectiveness of the proposed theoretical results.