Consensus for multiple heterogeneous Euler–Lagrange systems with time-delay and jointly connected topologies

In this paper, we consider the consensus problem of multiple agents modeled by Euler–Lagrange (EL) equation, among which two classes of agents are addressed, i.e., some agents with exactly known parameters and the others with parametric uncertainties. We propose a distributed consensus protocol for the heterogeneous EL systems in which both time-delay and jointly connected topologies are taken into consideration. Based on graph theory, Lyapunov theory and Barbalat?s lemma, the stability of the controller is proved. A distinctive feature of this work is to investigate the consensus problem of EL systems with heterogeneous dynamics, time-delay and jointly connected topologies in a unified theoretical framework. Simulation results are also provided to illustrate the effectiveness of the obtained results.     

Distributed coordination on state-dependent fuzzy graphs

Multiagent systems are increasingly becoming popular among researchers spanning multiple fields of study. However, existing studies only models communication interaction between agents as either fixed or switching topologies described by crisp graphs supported by algebraic graph theories. In this paper, we propose an alternative approach to describing agent interactions using fuzzy graphs. Our approach is aimed at opening up new research avenues and defining new problems in coordination control especially in terms of dynamics between agents’ states, graph topologies and coordination objectives. This paper studies distributed coordination on fuzzy graphs where the edge-weights modeling network topologies are dependent on the states of the agents in the network. In hindsight, the network weights are adjustable based on the situational state of the agents. First, we introduce the concept of fuzzy graphs and give some distinguishing features from the crisp or fixed graphs. Next, we provide some membership functions to define the state-dependent weights and finally we use some simulations to demonstrate the convergence of the proposed consensus algorithms especially for cases where the agents are subject to system failures.     

模块化生产网络的信任博弈及声誉激励研究

信任机制是模块化生产网络顺利运作的保障。研究了信任在模块化生产网络中的重要作用,分析了模块化生产网络的3种主要角色。构建了模块集成商与供应商之间的信任博弈模型,并建立了基于声誉的动态激励模型。结果表明：模块化生产网络的信任机制实质上是一种利益博弈问题,信任博弈具有帕累托改进性质的分配规则;声誉成为成员合作的一个有效激励,而动态激励机制注意到了模块供应商基于长期合作的声誉维护,起到了增强激励效应的作用。     

Interventional bipartite consensus on coopetition networks with unknown dynamics

In this paper, an interventional bipartite consensus problem is considered for a high-order multi-agent system with unknown disturbance dynamics. The interactions among the agents are cooperative and competitive simultaneously and thus the interaction network (just called coopetition network in sequel for simplicity) is conveniently modeled by a signed graph. When the coopetition network is structurally balanced, all the agents are split into two competitive subgroups. An exogenous system (called leader for simplicity) is introduced to intervene the two competitive subgroups such that they can reach a bipartite consensus. The unknown disturbance dynamics are assumed to have linear parametric models. With the help of the notation of a disagreement state variable, decentralized adaptive laws are proposed to estimate the unknown disturbances and a dynamic output-feedback consensus control is designed for each agent in a fully distributed fashion, respectively. The controller design guarantees that the state matrix of the closed-loop system can be an arbitrary predefined Hurwitz matrix. Under the assumption that the coopetition network is structurally balanced and the leader is a root of the spanning tree in an augmented graph, the bipartite consensus and the parameter estimation are analyzed by invoking a common Lyapunov function method when the coopetition network is time-varying according to a piecewise constant switching signal. Finally, simulation results are given to demonstrate the effectiveness of the proposed control strategy.     

Finite-time fuzzy game-based attitude control for on-orbit cooperative transporting

Substructure transporting is an important phase for on-orbit assembly. This paper investigates a problem of designing a control approach for multiple transporting agents attached to one substructure, so as to complete the task of attitude tracking and stabilization of the substructure in the transportation process. A finite-time fuzzy game control method is developed to solve this problem. Using the framework of differential game, a finite-time nonlinear game is formulated based on the individual performance index functions of agents and attitude dynamics of the combination consisting of transporting agents and the substructure, which can reflect the cooperation and coordination between agents. In order to realize finite-time convergence which is more suitable for engineering requirement, a speed function is introduced to transfer finite-time game into infinite-time game. Considering the limited computational ability of agents, Takagi-Sugeno (T-S) fuzzy is incorporated to divide the nonlinear game problem into weighted average of multiple linear games which are easy to get the Nash equilibrium. Numerical simulations validate the efficiency of the proposed method for attitude control and the advantage in less calculation and better performance in dynamics and steady state than the existing methods.     

基于知识质量的社会化问答社区用户知识共享的演化博弈分析

[目的/意义]社会化问答社区作为网络知识交互平台,其持续发展的关键在于促进用户知识共享,提升共享知识质量。[方法/过程]通过社区知识"质"与"量"的细分,将社区用户主动或被动参与社区知识共享获得的知识收益区分为"质"的收益与"量"的收益,并构建社会化问答社区用户知识共享的演化博弈模型,探讨不同博弈假设下问答社区知识共享的均衡状态。[结果/结论]通过仿真显示,社会化问答社区共享知识质量与用户共享行为策略会受到用户共享意愿与能力、用户认可与社区激励、感知共享成本等因素的影响。     

Containment control of heterogeneous fractional-order multi-agent systems

Fractional-order calculus has been studied deeply because many networked systems can only be described with fractional-order dynamics in complex environments. When different agents of networked systems show diverse individual features, fractional-order dynamics with heterogeneous characters will be used to illustrate the multi-agent systems (MAS). Based on the distinguishing behaviors of agents, a compounded fractional-order multi-agent systems(FOMAS) is presented with diverse dynamical equations. Suppose multiple leader agents existing in FOMAS, containment consensus control of FOMAS with directed weighted topologies is studied. By applying frequency domain analysis theory of the fractional-order operator, an upper bound of delays is obtained to ensure containment controls of heterogenous FOMAS with communication delays. The consensus results of delayed fractional-order dynamics in this paper can be expanded to the integer-order models. Finally, the results are verified by simulation examples.     

Distributed dynamic event-triggered consensus control for multi-agent systems under fixed and switching topologies

This paper is devoted to the dynamic event-triggered consensus problem of general linear multi-agent systems under fixed and switching directed topologies. Two distributed dynamic event-triggered strategies, where internal dynamic variables are involved, are introduced for each agent to achieve consensus asymptotically. Compared with the existing static triggering strategies, the purposed dynamic triggering strategies result in larger inter-execution times and less communication energy among agents. In addition, neither controller updates nor triggering threshold detections require continuous communication in the purposed control strategies. It is also proven that the Zeno behavior is strictly ruled out under fixed and switching directed topologies. Finally, the effectiveness of the theoretical analysis is demonstrated by numerical simulations.     

基于前景理论的重大疫情网络谣言管控三方演化博弈分析

【目的/意义】在COVID-19疫情背景下,出现了许多影响网络环境和社会秩序的网络谣言,研究与谣言传 播相关的群体对现实社会具有重要的意义。【方法/过程】文章通过对重大疫情下网络谣言等理论知识的研究,构建 了政府、网络运营商和造谣者三方演化博弈模型,根据前景理论将感知价值和实际价值的差值带入三方演化博弈 模型,优化模型的实际意义,并通过系统动力学软件进行仿真模拟。【结果/结论】三方博弈能否到达最优稳定点仅 与造谣者的感知价值的敏感度有关,且随着感知价值敏感度的变化三方博弈结果将会出现不稳定或稳定在其他非 最优稳定点上两种情况。最后对提出网络谣言管控的相关建议。【创新/局限】文章的创新之处在于在演化博弈模 型当中加入前景理论与传统的期望效用理论,增加了决策者的理性不足和偏好两方面的考虑,更加贴合舆情的实 际情况。     

Measuring project performance by applying social network analyses

It is often argued that the core of organizational success is efficient collaboration. Some authors even posit that efficient collaboration is more important to organizational innovation and performance than individual skills or expertise. However, the lack of efficient models to manage collaboration properly is a major constraint for organizations to profit from internal and external collaborative initiatives. Currently, much of the collaboration in organizations occurs through virtual network channels, such as e-mail, Yammer, Jabber, Microsoft Teams, Skype, and Zoom. These are even more important in situations where different time zones and even threats of a pandemic constrain face-to-face human interactions. This work introduces a multidisciplinary heuristic model developed based on project risk management and social network analysis centrality metrics graph-theory to quantitatively measure dynamic organizational collaboration in the project environment. A case study illustrates the proposed model's implementation and application in a real virtual project organizational context. The major benefit of applying this proposed model is that it enables organizations to quantitatively measure different collaborative, organizational, and dynamic behavioral patterns, which can later correlate with organizational outcomes. The model analyzes three collaborative project dimensions: network collaboration cohesion evolution, network collaboration degree evolution, and network team set variability evolution. This provides organizations an innovative approach to understand and manage possible collaborative project risks that may emerge as projects are delivered. Organizations can use the proposed model to identify projects' critical success factors by comparing successful and unsuccessful delivered projects' dynamic behaviors if a substantial number of both project types are analyzed. The proposed model also enables organizations to make decisions with more information regarding the support for changes in observed collaborative patterns as demonstrated by statistical models in general, and linear regressions in particular. Further, the proposed model provides organizations with a completely bias-free data-collection process that eliminates organizational downtime. Finally, applying the proposed model in organizations will reduce or eliminate the risks associated with virtual collaborative dynamics, leading to the optimized use of resources; this will transform organizations to become more lean-oriented and significantly contribute to economic, social, and environmental global sustainability.     

Observer-based mean square consensus of nonlinear networked systems under Markov switching communication topologies

This paper aims to investigate the mean square consensus (MSC) problem of a class of nonlinear networked systems subject to directed and stochastic switching communication topologies, where the switching law is determined by an ergodic continuous-time Markov process. The cooperative consensus controller is designed by using an observer-based method. Firstly, for the case with Lur’e nonlinear dynamics, by developing a stochastic Lyapunov function, we show that the MSC under consideration can be realized if the union of the underlying network graphs has a directed spanning tree. It is worth noting that none of the network graphs is required to contain a directed spanning tree. Moreover, we study the MSC problem for networked systems with Lipschitz-type nonlinear dynamics. Finally, a numerical simulation is conducted on multiple Chua’s circuit systems to illustrate the effectiveness of the proposed controllers.     

Practical consensus for heterophilous multiagent networks with constrained states

This paper studies practical consensus problems for continuous-time multiagent systems with heterophilous dynamics. Agents in the network tend to cease communication when they are close enough to each other inspired by the heterophily principle in social and opinion dynamics. We propose two types of state constraints on dynamical agents, in which the hard constraint refers to exogenous restrictions that rule the entire evolution of the agent behavior and the soft constraint turns out to be endogenous and only the ultimate equilibria of the agents are restricted. Sufficient conditions have been established to achieve practical consensus for both types of constrained systems. Numerical simulations show that the practical consensus time grows logarithmically, revealing a rapid convergence with respect to disagreement of initial conditions.     

Prescribed performance bipartite consensus for nonlinear agents with antagonistic interactions: A PI transformation approach

The leaderless, prescribed performance consensus problem for groups of agents with antagonistic interactions is addressed for the first time in this paper. We consider agents modeled by pure feedback nonlinear systems with unknown dynamics and an agent communication network described by a signed digraph with a directed spanning tree. A new proportional and integral (PI) variable transformation is proposed that enables the solution of the problem of leaderless bipartite consensus with prescribed performance by recasting it into a regulation problem with prescribed performance, which in turn we solve by a low complexity distributed control law. The algorithm guarantees uniform boundedness of all closed-loop signals and prescribed performance for the bipartite consensus error. Simulations verify the validity of our theoretical analysis.     

微博社区网民情绪引导与舆情控制的SIR演化博弈分析

【目的/意义】通过研究微博社区网民的情感交互与舆情观点,有助于在复杂的网络中掌握网民情绪演化从而良性引导网络舆情态势。【方法/过程】基于传播动力学、社会安全阀等理论,结合微博社区中的实际案例,定义事件利益主体并抽象出事件演化的全生命周期,同时,构建SIR演化博弈模型刻画网民情绪的动态演化规律及主体决策博弈演化过程,并通过仿真模拟分析得到系统演化至稳定状态的均衡条件。【结果/结论】结果表明:微博社区中意见领袖与官方媒体感知收益与风险的敏感度对决策行为产生显著影响;官方媒体及时设置有效议程构建安全阀能够防止网民情绪恶化;意见领袖与官方媒体的协同引导能够最大效度地帮助政府管控网络舆情。     

企业创新网络的自组织演化模型

 基于企业在创新合作活动中的理性决策行为,建立创新网络动态演化模型。运用复杂网络研究中的数值仿真方法,研究创新网络的动态演化规律、企业间的知识溢出与网络结构的关系。研究放宽了创新网络博弈论文献对网络结构的不合理假定,同时又赋予网络演化一定的经济含义。研究发现：创新网络可自组织演化至稳定状态;知识溢出效率的提高可增进行业的收益水平,并激励企业积极地参与创新合作,但过高的效率值会加剧企业间收益分配的不平等;知识溢出效率不影响企业合作规模的分布;稳定状态下的创新网络为小世界网络,具有高的聚集系数和短的平均路径长度,但其并非利用效率最高的网络安排。     

基于秩依效用理论的网络舆情传播博弈模型

针对当前研究的网络舆情传播问题,本文依据现有网络舆情的传播实例,归纳总结出网络舆情传播的新特征。探讨以“网络大V”为代表的舆情传播者和以政府部门为代表的网络舆情管控者之间的博弈模型,运用秩依效用理论,考虑博弈双方带有心理偏好时的博弈状态,依据不同的实际情况分析其Nash均衡。最后用实例对模型进行验证。     

情绪因素影响下的突发事件网络舆情演化研究

【目的/意义】突发事件容易催生舆情主体的各种情绪,诱发其情绪化行为和态度,分析网民和政府的情绪对其行为倾向的影响机制,有助于更好地把握舆情态势。【方法/过程】该文将包含情绪函数的RDEU理论引入突发事件网络舆情演化的博弈中,然后结合演化博弈中的复制动态方程,构建以网民和政府为代表的动态博弈模型并求解,最后通过假设收益数值对情绪影响下的演化过程进行仿真分析,得出相关结论和建议。【结果/结论】研究结果表明,不同的情绪类型和情绪强度不仅会改变最终的演化结果,而且还会影响群体策略的演化速度,导致舆情的不同演化趋势。     

进化稳定性对策与种群进化

进化稳定性对策,即ESS理论,是继达尔文进化论提出后,研究种群进化动态的最重要方法。1971年Maynard Smith首先提出进化稳定性对策,运用对策论模型分析种群的表现型和基因型的进化动态。研究种群的表现型动态主要是分析种群表现型分布的平衡点及其稳定性。自然选择的基本原则是适者生存,选择个体的标准是个体适宜性。在对策论模型中,个体的适宜性不仅受基因型控制,还要受到种群内部表现型分布的影响,这就是种群适宜性的表型依赖机制。     

基于动态博弈模型的企业与供应商项目关系管理

通过对博弈的边界条件进行界定,构筑了一个项目关系管理的三阶段斯坦克尔伯格(Stackelberg)动态博弈模型。对本博弈的子博弈精炼纳什均衡的分析认为:企业与项目供应商的非合作博弈中,供应商是否履约以及合同实施的程度取决于企业对博弈双方长远合作关系的引导,企业如果加强关系管理,将激励供应商按照企业要求的合同时序量履约,从而实现双方最优的策略选择——合作式竞争,最终实现企业自身的项目目标乃至战略目标。     

电子竞技运动在我国发展的可行性研究

电子竞技是利用信息技术为核心的软硬件设备作为器械进行的、在体育规则下实现的人与人之间的对抗性运动。电子竞技和网络游戏都属于电子游戏的范畴,但二者又有着本质的区别。现代信息社会发展电子竞技运动势在必行,同时也是建设我国小康社会的需要。为此．提出发展我国电子竞技的对策。