Mean-square composite-rotating consensus of second-order systems with communication noises

We study the mean-square composite-rotating consensus problem of second-order multi-agent systems with communication noises, where all agents rotate around a common center and the center of rotation spins around a fixed point simultaneously. Firstly, a time-varying consensus gain is introduced to attenuate to the effect of communication noises. Secondly, sufficient conditions are obtained for achieving the mean-square composite-rotating consensus. Finally, simulations are provided to demonstrate the effectiveness of the proposed algorithm.

Mean Square Average Generalized Consensus of Multi-Agent Systems Under Time-Delays and Stochastic Disturbances

Compared with the traditional consensus problem, this paper deals with the mean square average generalized consensus(MSAGC) of multi-agent systems under fixed directed topology, where all agents are affected by stochastic disturbances. Distributed protocol depending on delayed time information from neighbors is designed. Based on Lyapunov stability theory, together with results from matrix theory and It o s derivation theory, the linear matrix inequalities approach is used to establish sufficient conditions to ensure MSAGC of multi-agent systems. Finally, numerical simulations are provided to illustrate the theoretical results.

Mean square consensus of uncertain discrete-time stochastic multi-agent systems with x-dependent noise

In this paper, the mean square consensus control problem is investigated for linear uncertain discrete-time multi-agent systems withx-dependent noise and time-varying delays. Under undirected connected topology, the robust consensus problem of multi-agent systems is converted into the robust stabilisation problem for thediscrete-time stochastic systems. By utilising Lyapunov functionaland the linear matrix inequality method, some new sufficient conditions are derived to guarantee the consensus of uncertain discretetime stochastic multi-agent systems. Based on the state feedbackcontroller protocol with time-varying delays, a new consensus criterion is established for the discrete-time stochastic multi-agent systems with parameter uncertainties. Finally, numerical examples areprovided to illustrate the effectiveness of the proposed results.

随机拓扑下离散多智能体事件触发一致性

针对离散时间多智能体跟踪不稳定的问题,本文研究离散多智能体系统的事件触发一致性控制问题,通过马尔可夫跳变拓扑结构实现各智能体间的信息交互,设计了一种基于动态响应的事件触发条件,给出了马尔可夫跳变控制协议,构造带有转移概率的离散Lyapunov函数,得到所有智能体是均方一致性的充分条件。数值算例验证了所提方法的有效性,证明了本结论可用于解决随机拓扑下离散多智能体的跟踪不一致问题。

基于合作-竞争关系马尔可夫切换下异质多智能体系统均方二分组一致研究

针对马尔可夫切换下离散和连续异质多智能体系统均方二分组一致问题,本文分别构造了2类包含合作竞争关系和马尔可夫切换拓扑结构异质多智能体系统均方二分组一致协议。利用随机不可约非周期矩阵(stochastic indecomposable and aperiodic matrices,SIA)相关性质、图论代数和矩阵分析等理论,得到相关系统实现均方二分组一致的充分必要条件。仿真实例说明了理论结果的有效性。

线性均方一致性问题的偏差估计

多智能体协同在传感网、社交网、分布式控制等诸多领域有着广泛的实际应用背景,一致性问题作为多智能体协同的基础,受到越来越多研究者的关注.在实际环境中,由于设备、通信干扰等诸多原因,信息在传递过程中通常会携有噪声,本文对噪声条件下一致性问题的系统偏差进行了研究,将求解一致性协议噪声偏差问题转化成矩阵范数的积分问题,根据矩阵迹与特征值的关系,利用范数不等式及积分中值定理,给出仅与增益函数和网络结构相关的一致性协议噪声偏差上界,为一致性系统在实际应用中的噪声估计奠定了理论基础.

适用于稀疏动态无线传感器网络的并行融合分布式无迹信息滤波算法

稀疏和随机动态变化是实际无线传感器网络(wireless sensor network,WSN)中普遍共同存在的两种通信拓扑不稳定因素,使基于一致性算法的分布式无迹信息滤波(distributed unscented information filter,DUIF)算法适用于稀疏动态WSN,将极大提高其实用性.为此,本文提出一种并行融合DUIF(parallel fusion DUIF,PF–DUIF)算法.在PF–DUIF算法中,通过将实时局部后验估计均值和协方差用于局部无迹信息滤波器(local unscented information filter,LUIF)的Sigma点采样,使LUIF和加权平均一致性滤波器(weighted average consensus filter,WACF)得以并行运行,从而有效抵制由通信拓扑随机动态变化带来的较大一致跟踪误差的困扰;同时,WACF通过对LUIF输出的无偏局部信息矩阵和向量分别进行平均一致性滤波,最终得到不包含由稀疏通信拓扑引起的平均一致误差的分布式后验估计结果;进而,建立即时更新机制有效抑制随机动态通信拓扑引起的PF–DUIF算法滤波异步问题,同时,基于稀疏动态WSN的平均网络模型,在通信能量消耗受限条件下优化WACF均方收敛速率,从而提高PF–DUIF算法的整体滤波效率.仿真实验结果表明,PF–DUIF算法能够有效应用于稀疏动态WSN机动目标跟踪.

线性离散一致性系统均方偏差上界

一致性问题是多智能体协同控制的基础,有广泛的应用背景。之前的研究已经给出了线性一致性协议实现均方一致的充分条件,在此基础上,对线性离散均方一致性问题进行了更加深入的讨论,以图论和随机分析为基础,进一步给出并严格证明线性离散均方一致性问题噪声偏差的上界,同时以4种不同的网络拓扑为例,对所提理论结果进行了说明。结论将为线性离散一致性系统的噪声估计和控制提供理论依据。

带有随机噪声和时滞的多自主体系统均方趋同控制

该文研究了有领导者的固定和切换拓扑结构下带有随机噪声和通讯时滞的多自主体系统均方趋同控制。为了降低噪声强度,使跟随者及时更新信息,与变速度的领导者保持一致,设计了一个基于观测器的变增益的分布式控制器。在该控制器作用下,利用Lyapunov函数、随机分析方法和图论知识,得到多自主体系统均方趋同的充分条件和允许的最大时滞上界。仿真示例说明了该文方法的有效性。

基于噪声的非线性多智能体系统的均方一致性

本文主要研究拓扑结构为有向强连接的非线性多智能体系统的均方一致性问题.考虑到非线性系统中的个体在传递信息时受到噪声环境的干扰,提出一种新的延迟控制方案,提高了系统的控制性能.基于Lyapunov稳定性和Ito^积分方程理论,得到多智能体系统渐近趋于均方一致的充分条件.同时,得到相同的耦合强度下容许的最大延迟间隔,数值仿真结果进一步验证了理论分析的有效性.

一种基于事件触发的分布式卡尔曼一致性滤波算法设计

针对无线传感器网络中存在的通信带宽受限等问题,设计了基于事件触发的分布式卡尔曼一致性滤波算法。采用增量发送的传输机制,每个传感器仅在当前时刻观测值与最新触发时刻观测值之间的差值平方超出阈值时,才将观测值发送到相应的估值器。而每个估值器都可以通过时间触发的规则从其邻居节点接收到估计值。为了避免估值之间互协方差矩阵的计算,通过局部最小化方差的上界提出了事件触发的分布式卡尔曼滤波器。基于李雅普诺夫方法证明了算法在均方意义下的指数有界性。数值仿真验证了事件触发的阈值越小,通信率越高且滤波器的估计精度越高;否则,通信率越低且估计精度越低。

随机干扰情形下多自主体系统的一致性

文章研究了具有一般通信拓扑和带有随机通信噪声的多自主体系统的一致性问题。为了克服随机噪声的影响,该文设计了随机分布式协议。在该协议下,该文基于广义代数连通性,利用微分方程稳定性理论、矩阵分析、随机分析方法以及图论相关知识,得到了多自主体系统达到均方一致的充分条件以及最终收敛状态;最后通过仿真验证了结果的有效性。

输入约束下高阶智能体均方一致性研究

该文对高阶多智能体系统拓扑结构Markov切换且控制输入受到非凸约束时的均方一致性问题进行了研究。首先,引入了一个非凸约束算子,并且根据多智能体系统中邻居节点信息设计了系统的控制协议;之后,利用非负矩阵的性质,得到了当高阶多智能体系统拓扑结构是Markov切换时,系统能够实现均方一致性的充要条件;最后,通过数值仿真验证了在该控制协议下,系统能够达到均方一致性。

具有随机丢包的网络化多自主体系统的一致性

研究具有随机丢包的网络化多自主体系统的均方一致性问题,其中不同自主体间的通信通道具有相同的丢包情况并且均服从马尔可夫(Markov)分布。首先,利用系统变换和迭代方法,得到了系统达到均方一致的一个初等充要条件。然后,利用矩阵理论和图论知识,如果拓扑图含有有向生成树,则可以将系统的均方一致性转化成一个线性Markov跳变系统的均方稳定性,并且可以建立线性矩阵不等式形式的均方可镇定准则。最后,通过相应的仿真实例说明了所得结论的可行性。

脉冲控制下半马尔可夫随机MAS的均方一致性

针对多智能体系统会受到外部干扰而使系统结构随机发生改变的问题,提出了当切换信号服从半马尔可夫过程时,领导-跟随随机多智能体系统实现均方一致的控制算法。运用代数图论、半马尔可夫过程的转移速率与驻留时间分布函数相关性质和Lyapunov稳定性理论等理论工具,对控制算法进行了稳定性分析。通过设计合适的脉冲控制协议,给出了系统实现均方一致性的充分条件。条件表明,若脉冲间隔具有某一上界,均方一致性仍可实现。最后,数值仿真验证了结果的有效性。

基于事件驱动的随机多智能体系统的一致性

文章使用一种分布式事件驱动控制策略,对带领导者的线性随机多智能体系统的均方一致性进行了研究。利用微分方程稳定性理论与随机分析方法证明了在该事件驱动控制策略下,带领导者的线性随机多智能体系统可达均方一致且无芝诺行为发生,且不要求跟随者之间的拓扑结构是连通图;最后通过仿真,验证了该策略可有效减少多智能体间的通讯次数。

两种不同脉冲欺骗攻击下随机多智能体系统的均方拟一致性

考虑实际应用中多智能体系统所处环境的复杂性及相关通信网络的开放性,主要研究了一类随机多智能体系统在受到两种不同脉冲欺骗攻击情况下实现均方拟一致性问题。所考虑的两种脉冲欺骗攻击包括替代攻击和虚假数据注入攻击,而攻击发生的概率则是通过引入服从伯努利分布的随机变量加以描述。结合随机过程相关知识和李雅普诺夫稳定性理论,给出系统实现均方拟一致性充分条件。条件表明,系统在遭受两种不同攻击的情况下,误差能保持在一定的范围内。最后,数值仿真验证了结果的有效性。

A novel gain design method to improve the consensus performance of output-feedback multi-agent systems

This paper considers the consensus problem of a group of homogeneous agents. These agents are governed by a general linear system and can only directly measure the output, instead of the state. In order to achieve the consensus goal, each agent estimates its state through a Luenberger observer, exchanges its estimated state with neighbors, and constructs the control input with the estimated states of its own and neighbors. Due to the existence of observation and process noises, only practical consensus, instead of asymptotical consensus, can be achieved in such multi-agent systems. The performance of the achieved practical consensus can be measured by the ultimate mean square deviation of the states of agents. That performance is closely related to the observation gains of the state observers and the control gains of agents. This paper proposes a method to optimize such performance with respect to the concerned observation and control gains. That method starts with a set of feasible observation and control gains and formulates a group of linear matrix inequalities （LMIs）. Solving these LMIs gives some intermediate matrix variables. By perturbing observation and control gains, and the intermediate matrix variables, the original LMIs yield another group of LMIs, which can be solved to provide a descent direction of observation and control gains. Moving along that descent direction, observation and control gains can be improved to yield better performance and work as the starting point of the next iteration. By iteratively repeating this procedure, we can hopefully improve the consensus performance of the concerned multi-agent system. Simulations are done to demonstrate the effectiveness of the proposed method.

Reaching a stochastic consensus in the noisy networks of linear MIMO agents:Dynamic output-feedback and convergence rate

This paper addresses the leader-following consensus problem of linear multi-agent systems(MASs) with communication noise. Each agent's dynamical behavior is described by a linear multi-input and multi-output(MIMO) system, and the agent's full state is assumed to be unavailable. To deal with this challenge, a state observer is constructed to estimate the agent's full state. A dynamic output-feedback based protocol that is based on the estimated state is proposed. To mitigate the effect of communication noise, noise-attenuation gains are also introduced into the proposed protocol. In this study, each agent is allowed to have its own noise-attenuation gain. It is shown that the proposed protocol can solve the mean square leader-following consensus problem of a linear MIMO MAS. Moreover, if all noise-attenuation gains are of Q(t-β), where b∈(0,1), the convergence rate of the MAS can be quantitatively analyzed. It turns out that all followers' states converge to the leader's state in the mean square sense at a rate of O(t-β).