基于有向通信拓扑的高阶分布式一致性算法

为了提高有向通信拓扑下分布式一致性算法的收敛速度,提出了一种基于有向通信拓扑的高阶分布式一致性算法。该算法通过有向单跳通信,利用有向二跳邻接节点的前多步信息提高分布式一致性算法的收敛速度。对有向通信拓扑下该算法的收敛性能和收敛速度进行了分析和仿真比较。结果显示,该算法在满足一定条件下能收敛到初始状态的平均值,与其他同样利用二跳邻接节点信息的一致性算法相比,具有通信量小、收敛速度更快的特点,但是能容忍的最大通信延时变小。     

多智能体系统分布式一致性算法研究现状

综述了多智能体系统分布式一致性问题的研究现状。从理论层面介绍了一致性问题的几种常见定义及与特性相关的主要参数;总结归纳了近年来几种一致性协议及其理论分析结果;分析和阐述了一致性问题的主要应用领域的进展。展望了未来的研究方向。     

伪多跳中继分布式一致性算法

为了提高分布式一致性问题的收敛速度, 减少节点间的通信成本, 本文提出一种分布式一致性算法. 新算法采用单跳通信, 利用非邻接节点的前状态信息进行节点状态更新. 本文证明了在无向通信拓扑下新算法的一致性收敛, 并分析计算了新算法的通信量以及收敛速度的变化. 通过理论分析和仿真验证, 结果表明新算法具有收敛速度快, 通信方式简单, 通信量少, 存在通信延时情况下通信数据不容易丢失等优点.     

离散线性一致性算法噪声问题研究

多智能体一致性问题在传感网、社交网、协同控制等诸多领域有着广泛的实际应用背景, 本文对离散线性一致性算法的噪声问题进行了研究, 证明了离散线性 一致性算法的噪声不可控性; 提出基于抑噪算子ε(t)的噪声控制策略, 指出当ε(t)为t-0.5的高阶无穷小时, 抑噪后的一致性算法噪声可控; 分析了抑噪算子对一致性 算法收敛性的影响, 证明了在无噪声条件下, 当抑噪算子ε(t为t-1的低阶无穷小时, 抑噪后的一致性算法依然可以使Agent收敛至原收敛状态x*.在上述结论基础上进一步指出, 当t→∞ 时, 若抑噪算子ε(t)的阶在t-0.5~t-1之间, 所有Agent 的状态将以原收敛状态x* 为中心呈正态分布. 最后, 以DHA 为例对相应理论结果进行了验证和讨论. 本文为线性一致性算法的噪声控制提供了理论依据, 对抑噪算s子的确定有较强的指导意义.     

多智能体系统离散时间一致性问题中的参数设计

研究二阶多智能体系统在固定、有向通信拓扑条件下的离散时间一致性问题.针对每个智能体,采用基于速度、位置的分布式控制协议,分析速度、位置增益系数以及采样周期等参数对系统一致性的影响.结果表明,要实现二阶多智能体系统的一致性控制,在保持个体之间通信拓扑有生成树的同时,控制协议中的各个参数还必须满足一定的约束条件.系统拉普拉斯矩阵的特征值和采样周期对系统能否实现渐近一致起到了关键的作用.最后,给出一致性控制中参数设计的一种可行方法,并通过仿真对理论分析的正确性进行验证.     

离散异质多智能体系统的分组一致性控制

针对一类混合异质多智能体系统的分组一致性控制问题进行了研究。具体分析了由一阶智能体和二阶智能体组成的混合异质系统,研究其在离散情况下的分组一致性。基于两个合理的假设提出了线性控制协议,运用代数图论、稳定性理论和矩阵理论,分析协议作用下闭环系统的系统矩阵及动态特性,取得了系统渐近实现分组一致性的充分条件,该条件与系统拓扑结构、采样周期以及控制参数有关。结论同时适用于有向拓扑与无向拓扑,最后通过仿真实例对所得分析结果进行了验证。     

离散异构线性多智能体系统的输出一致性

研究了离散异构线性多智能体系统的输出一致性问题。考虑到实际系统中部分跟随智能体不能准确获知领航智能体的动态,所以采用观测器方法研究系统的输出一致性问题。首先为每个跟随智能体构造观测器,用以估计领航者的状态;其次设计分布式控制器,提出系统实现输出一致性所应满足的充分条件,并运用Lyapunov稳定性理论、矩阵论、图论知识加以证明;最后运用MATLAB软件的SIMULINK工具箱进行数值仿真,仿真结果显示文中方法与结论是有效的。     

基于Q-learning的离散时间多智能体系统一致性

针对模型未知的一类离散时间多智能体系统,本文提出了一种Q-learning方法实现多智能体系统的一致性控制.该方法不依赖于系统模型,能够利用系统数据迭代求解出可使给定目标函数最小的控制律,使所有智能体的状态实现一致.通过各个智能体所产生的系统数据,采用策略迭代的方法实时更新求解得到多智能体系统的控制律,并对所提Q-le...     

离散时间系统的多智能体的一致性

动态多智能体系统的一致性是复杂动力学系统中很有现实意义的问题.假设智能体连接网络拓扑是无向、固定和连通的,而且个体之间信息传递存在通信时廷,分析了一个动态移动多智能体离散时间系统.应用广义Nyquist判据研究具有通信时延的多智能体离散时间系统,得到了保证系统达到一致的充分条件.最后应用计算机仿真验证了该结论的有效性.     

离散多智能体系统分组一致性研究

针对一阶、二阶离散多智能体系统的分组一致性问题进行了研究。为了去除了现有结果的一些保守假设,提出了两个新的分组一致性协议,新的协议反映了系统相同分组内与不同分组间智能体的相互影响。基于图论和稳定性理论,经过分析得到了两个保证系统一致的充分条件。最后,通过两个仿真实例证明了理论结果的有效性。     

Output consensus for high-order linear time-invariant swarm systems

Output consensus analysis and design problems of high-order linear time-invariant swarm systems are investigated. First, an output consensus subspace and a complement output consensus subspace are introduced. By output projection onto the two subspaces and the partial stability theory, a necessary and sufficient condition for output consensus is presented, and an explicit expression of the output consensus function, which is jointly determined by the interaction topology and initial states of all agents, is given. Especially, it is shown that the output consensus function is completely determined by initial states of all agents if the interaction topology is balanced. Then, an approach to determine gain matrices in consensus protocols of swarm systems is proposed, which has less calculation complexity. Finally, a numerical example is shown to demonstrate theoretical results.     

Consensus-based distributed optimisation of multi-agent networks via a two level subgradient-proximal algorithm

This paper presents a consensus-based stochastic subgradient algorithm for multi-agent networks to minimise multiple convex but not necessarily differential objective functions, subject to an intersection set of multiple closed convex constraint sets. Compared with the existing results an alternative subgradient algorithm is first introduced based on two level subgradient iterations, where the first level is to minimise the component functions, and the second to enforce the iterates not oscillate from the constraint set wildly. In addition, a distributed consensus-based type of the proposed subgradient algorithm is constructed within the framework of multi-agent networks for the case when the iteration index of local objective functions and local constraint sets is not homologous. Detailed convergence analysis of the proposed algorithms is established using matrix theories and super-martingale convergence theorem. In addition, a pre-step convergence factor is obtained in this study to characterise the distance between the iterations and the optimal set, while some existing literatures only present a convergence work. Simulation results are given to demonstrate the effectiveness of the developed theoretical results.     

分布式传感网络的自适应一致性算法

为了提高分布式传感网络的估计精度,提出了一种新的自适应一致性算法。该算法在每次迭代时只需部分节点工作,即进行目标状态的监测。通过节点之间二进制信息的交换来调整每次迭代时的一致性权值,使得每次迭代时工作节点所占的权值更大,进而将该一致性算法与卡尔曼滤波相结合对目标状态进行估计。对该算法进行数值仿真,并与其他一致性加权算法进行比较,验证了该算法的有效性。     

Reaching a consensus in networks of high-order integral agents under switching directed topologies

Consensus problem of high-order integral multi-agent systems under switching directed topology is considered in this study. Depending on whether the agent’s full state is available or not, two distributed protocols are proposed to ensure that states of all agents can be convergent to a same stationary value. In the proposed protocols, the gain vector associated with the agent’s (estimated) state and the gain vector associated with the relative (estimated) states between agents are designed in a sophisticated way. By this particular design, the high-order integral multi-agent system can be transformed into a first-order integral multi-agent system. Also, the convergence of the transformed first-order integral agent’s state indicates the convergence of the original high-order integral agent’s state, if and only if all roots of the polynomial, whose coefficients are the entries of the gain vector associated with the relative (estimated) states between agents, are in the open left-half complex plane. Therefore, many analysis techniques in the first-order integral multi-agent system can be directly borrowed to solve the problems in the high-order integral multi-agent system. Due to this property, it is proved that to reach a consensus, the switching directed topology of multi-agent system is only required to be ‘uniformly jointly quasi-strongly connected’, which seems the mildest connectivity condition in the literature. In addition, the consensus problem of discrete-time high-order integral multi-agent systems is studied. The corresponding consensus protocol and performance analysis are presented. Finally, three simulation examples are provided to show the effectiveness of the proposed approach.     

Discrete-time multi-agent consensus with quantization and communication delays

In this paper, discrete-time multi-agent consensus problem with quantization and communication delays is investigated. A new discrete-time multi-agent consensus model is considered in which each agent can only receive the delayed quantized information from its neighbors. In the presence of quantization and communication delays, it is shown that the multi-agent network can achieve consensus under the connectivity network topology. Simulation examples are also provided to demonstrate the correctness of the theoretical results.     

Stationary average consensus protocol for a class of heterogeneous high-order multi-agent systems with application for aircraft

This paper investigates the stationary average consensus problem for a class of heterogeneous-order multi-agent systems. The goal is to bring the positions of agents to the average of their initial positions while letting the other states converge to zero. To this end, three different consensus protocols are proposed. First, based on the auxiliary variables information among the agents under switching directed networks and state-feedback control, a protocol is proposed whereby all the agents achieve stationary average consensus. In the second and third protocols, by resorting to only measurements of relative positions of neighbouring agents under fixed balanced directed networks, two control frameworks are presented with two strategies based on state-feedback and output-feedback control. Finally, simulation results are given to illustrate the effectiveness of the proposed protocols.     

分布式一致性算法Yac

传统静态拓扑主从模型分布式一致性算法存在严重负载不均及单点性能瓶颈效应,且崩溃节点大于集群规模的50%时算法无法正常工作。针对上述问题,提出基于动态拓扑及有限表决思想的分布式一致性算法（Yac）。算法动态生成参与一致性表决的成员子集及Leader节点并时分迁移,形成统计负载均衡;去除要求全体多数派成员参与表决的强约束,使算法具备更高的失效容忍性;并通过日志链机制重新建立算法安全性约束,同时证明了算法的正确性。实验结果表明,改进算法的单点负载集中效应显著低于主流静态拓扑主从模型分布式一致性算法Zookeeper;改进算法失效容忍性优于Zookeeper,且最坏情况下与Zookeeper算法保持持平;同等集群规模下,改进算法比Zookeeper拥有更高吞吐量上限。