Fixed-Time Antidisturbance Consensus Tracking for Nonlinear Multiagent Systems With Matching and Mismatching Disturbances

In this paper,fixed-time consensus tracking for mul-tiagent systems(MASs)with dynamics in the form of strict feed-back affine nonlinearity is addressed.A fixed-time antidistur-bance consensus tracking protocol is proposed,which consists of a distributed fixed-time observer,a fixed-time disturbance observer,a nonsmooth antidisturbance backstepping controller,and the fixed-time stability analysis is conducted by using the Lyapunov theory correspondingly.This paper includes three main improvements.First,a distributed fixed-time observer is developed for each follower to obtain an estimate of the leader’s output by utilizing the topology of the communication network.Second,a fixed-time disturbance observer is given to estimate the lumped disturbances for feedforward compensation.Finally,a nonsmooth antidisturbance backstepping tracking controller with feedforward compensation for lumped disturbances is designed.In order to mitigate the“explosion of complexity”in the tradi-tional backstepping approach,we have implemented a modified nonsmooth command filter to enhance the performance of the closed-loop system.The simulation results show that the pro-posed method is effective.

Adaptive Event-Triggered Time-Varying Output Group Formation Containment Control of Heterogeneous Multiagent Systems

In this paper,a class of time-varying output group formation containment control problem of general linear hetero-geneous multiagent systems(MASs)is investigated under directed topology.The MAS is composed of a number of tracking leaders,formation leaders and followers,where two different types of leaders are used to provide reference trajectories for movement and to achieve certain formations,respectively.Firstly,compen-sators are designed whose states are estimations of tracking lead-ers,based on which,a controller is developed for each formation leader to accomplish the expected formation.Secondly,two event-triggered compensators are proposed for each follower to evalu-ate the state and formation information of the formation leaders in the same group,respectively.Subsequently,a control protocol is designed for each follower,utilizing the output information,to guide the output towards the convex hull generated by the forma-tion leaders within the group.Next,the triggering sequence in this paper is decomposed into two sequences,and the inter-event intervals of these two triggering conditions are provided to rule out the Zeno behavior.Finally,a numerical simulation is intro-duced to confirm the validity of the proposed results.

Adaptive Cyber Defense Technique Based on Multiagent Reinforcement Learning Strategies

The static nature of cyber defense systems gives attackers a sufficient amount of time to explore and further exploit the vulnerabilities of information technology systems.In this paper,we investigate a problem where multiagent sys-tems sensing and acting in an environment contribute to adaptive cyber defense.We present a learning strategy that enables multiple agents to learn optimal poli-cies using multiagent reinforcement learning(MARL).Our proposed approach is inspired by the multiarmed bandits(MAB)learning technique for multiple agents to cooperate in decision making or to work independently.We study a MAB approach in which defenders visit a system multiple times in an alternating fash-ion to maximize their rewards and protect their system.We find that this game can be modeled from an individual player’s perspective as a restless MAB problem.We discover further results when the MAB takes the form of a pure birth process,such as a myopic optimal policy,as well as providing environments that offer the necessary incentives required for cooperation in multiplayer projects.

基于MultiAgent和CSCW的多媒体教学模型

生于MultiAgent和CSCW的观点，提出一个多媒体教学模型．探讨了该模型的结构以及模型中Agent的表示、通信等关键技术。

基于Multiagent生态进化算法实现个性化主动信息服务

网络资源的指数膨胀，使得用户在获得自己需要的信息资源时要花费大量的时间和精力。该文采用Multiagent技术协助用户获取需要的信息资源，以节省用户的时间。同时给出了一种生态进化算法，使得Agent可以逐渐把握用户的需求、适应用户需求的变化、探索用户可能感兴趣的领域，为用户提供个性化的主动信息服务。

Multiagent系统通讯及单元可靠性综合评测

为了更准确的评测Multiagent系统可靠性,提出了将通讯可靠性、单元可靠性及系统拓扑结构都考虑在评测过程中的综合评测方法.该方法概括出Multiagent系统的结构,釆用基于马尔可夫过程的方法,分析通讯可靠性、单元可靠性等因素对集中式、分布式及联邦式Multiagent系统可靠性的影响,并给出了系统可靠性评测模型.仿真结果表明,该方法能够将复杂系统的可靠性评测问题分解为简单的形式.针对各分解后的结构进行评测,进一步得到了整个系统的可靠性.

网络信息发现系统中Multiagent的研究与设计

随着因特网的快速发展 ,分布式应用开发越来越受到人们的重视。而现有的几种分布计算技术的不足已逐渐显露。本文介绍了 Multiagent技术及其发展 ,提出了一种以 Multiagent技术为基础 ,通过多个分布 Agent之间的协作共同完成网络信息发现的系统设计思想。

定题检索MultiAgent的研究与设计

本文详细介绍了定题检索Agent的系统结构和技术特点。通过对Agent的信息搜索原理以及关键技术的剖析,提出了一种以Multiagent技术为基础,多个分布Agent之间的协同工作,共同完成网络信息发现的系统设计思想。

应用MultiAgent优化自主导航系统

本文针对自主导航系统的多任务执行时容易产生的冲突问题,将系统进行功能分解,采用MultiAgent技术,由各个智能体共同执行完成系统功能。着重介绍了系统中各个代理的个体规划和社会规划,对各个Agent的可能产生的行为冲突和资源冲突进行了分析并提出相应避让策略。系统的实现采用了多线程技术,各个Agent之间的通信采用KQML的简化集。改进后的系统性能良好,无冲突现象。

基于Multiagent的社交网络舆情传播模型

为分析与预测社交网络中舆情传播过程和演化趋势,本文基于Multiagent分布式技术,建立了社交网络舆情传播模型,研究社交网络舆情传播方式,并根据Agent的行为和状态特性,对Agent进行分类,分析Agent的属性特征,通过剖析Agent个体之间的交互微观行为,依据信息价值时效性、信息源覆盖率和信息关注度,对转发行为的影响进行讨论,给出了Agent的决策规则,以网络结构方式体现Agent之间的内在联系。为验证模型的有效性,基于Agent的仿真平台,采用Netlogo 6.0.4进行模拟仿真分析。仿真结果表明,舆情信息的传播与信息价值呈正相关,信息价值越大,越有利于舆情信息的传播,且信息源的覆盖率与转发Agent数和中止Agent数成正相关,信息关注度对舆情传播影响甚大,说明该模型能准确分析和预测网络中舆情传播的演化趋势。该研究对相关人员了解社交网络中舆情的传播具有重要意义。

Limited-Budget Consensus Design and Analysis for Multiagent Systems With Switching Topologies and Intermittent Communications

This paper investigates limited-budget consensus design and analysis problems of general high-order multiagent systems with intermittent communications and switching topologies.The main contribution of this paper is that the tradeoff design between the energy consumption and the consensus performance can be realized while achieving leaderless or leaderfollowing consensus,under constraints of limited budgets and intermittent communications.Firstly,a new intermittent limitedbudget consensus control protocol with a practical trade-off design index is proposed,where the total budget of the whole multiagent system is limited.Then,leaderless limited-budget consensus design and analysis criteria are derived,in which the matrix variables of linear matrix inequalities are determined according to the total budget and the practical trade-off design parameters.Meanwhile,an explicit formulation of the consensus function is derived to describe the consensus state trajectory of the whole system.Moreover,a new two-stage transformation strategy is utilized for leader-following cases,by which the dynamics decomposition of leaderless and leader-following cases can be converted into a unified framework,and sufficient conditions of the leader-following limited-budget consensus design and analysis are determined via those of the leaderless cases.Finally,numerical simulations are given to illustrate theoretical results.

Multiagent flocking with formation in a constrained environment

We consider the problem of controlling a group of mobile agents to form a designated formation while flocking within a constrained environment. We first propose a potential field based method to drive the agents to move in connection with their neighbors, and regulate their relative positions to achieve the specific formation. The communication topology is preserved during the motion. We then extend the method to flocking with environmental constraints. Stability properties are analyzed to guarantee that all agents eventually form the desired formation while flocking, and flock safely without collision with the environment boundary. We verify our algorithm through simulations on a group of agents performing maximum coverage flocking and traveling through an unknown constrained environment.

Power source–power grid coordinated typhoon defense strategy based on multiagent dynamic game theory

A power source–power grid coordinated typhoon defense strategy is proposed in this study to minimize the cost of power grid anti-typhoon reinforcement measures and improve defense efficiency.It is based on multiagent dynamic game theory.This strategy regards a typhoon as a rational gamer that always causes the greatest damage.Together with the grid planner and black start unit(BSU)planner,it forms a multiagent defense–attack–defense dynamic game model naturally.The model is adopted to determine the optimal reinforcements for the transmission lines,black start power capacity,and location.Typhoon Hato,which struck a partial coastal area in Guangdong province in China in 2017,was adopted to formulate a step-by-step model of a typhoon attacking coastal area power systems.The results were substituted into the multiagent defense–attack–defense dynamic game model to obtain the optimal transmission line reinforcement positions,as well as optimal BSU capacity and geographic positions.An effective typhoon defense strategy and minimum load shedding were achieved,demonstrating the feasibility and correctness of the proposed strategy.The related theories and methods of this study have positive significance for the prevention of uncertain large-scale natural disasters.

Distributed model predictive control for multiagent systems with improved consistency

This paper addresses an improved distributed model predictive control （DMPC） scheme for multiagent systems with an attempt to improving its consistency. The deviation between what an agent is actually doing and what its neighbors believe that agent is doing is penalized in the cost function of each agent. At each sampling instant the compatibility constraint of each agent is set tighter than the previous sampling instant. Like the traditional approach, the performance cost is utilized as the Lyapunov function to prove closed-looped stability. The closed-loop stability is guaranteed if the weight matrix for deviation in the cost function are sufficiently large. The proposed distributed control scheme is formulated as quadratic programming with quadratic constraints. A numerical example is given to illustrate the effectiveness of the proposed scheme.

Enabling Technology of Multiagent Manufacturing System:A Novel Mode of Self-organizing IoT Manufacturing

As the manufacturing mode focuses more on network and community,the orders and production processes are becoming highly dynamic and unpredictable.The traditional manufacturing system cannot handle those exceptional events such as rush orders and machine breakdowns.Nevertheless,the multiagent manufacturing system(MAMS)becomes a critical pattern to deal with these disturbances in a real-time way.However,due to the lack of universality,MAMS is difficult to be applied to industrial sites.A new multiagent architecture and the relay cooperation model based on a positive process relation matrix are proposed to address this paper’s issue.An optimized contract net protocol(CNP)-based negotiation mechanism is developed to improve the efficiency of collaboration in the proposed architecture.Finally,a case study of self-organizing internet of things(Io T)manufacturing system is used to test the feasibility and effectiveness of the method.It is shown that the proposed self-organizing Io T manufacturing mode outperforms the traditional manufacturing system in terms of makespan and critical machine workload balancing under disturbances through comparison.

A Personalized Search Model Using Online Social Network Data Based on a Holonic Multiagent System

Personalized search utilizes user preferences to optimize search results,and most existing studies obtain user preferences by analyzing user behaviors in search engines that provide click-through data.However,the behavioral data are noisy because users often clicked some irrelevant documents to find their required information,and the new user cold start issue represents a serious problem,greatly reducing the performance of personalized search.This paper attempts to utilize online social network data to obtain user preferences that can be used to personalize search results,mine the knowledge of user interests,user influence and user relationships from online social networks,and use this knowledge to optimize the results returned by search engines.The proposed model is based on a holonic multiagent system that improves the adaptability and scalability of the model.The experimental results show that utilizing online social network data to implement personalized search is feasible and that online social network data are significant for personalized search.

Multiagent Reinforcement Learning:Rollout and Policy Iteration

We discuss the solution of complex multistage decision problems using methods that are based on the idea of policy iteration(PI),i.e.,start from some base policy and generate an improved policy.Rollout is the simplest method of this type,where just one improved policy is generated.We can view PI as repeated application of rollout,where the rollout policy at each iteration serves as the base policy for the next iteration.In contrast with PI,rollout has a robustness property:it can be applied on-line and is suitable for on-line replanning.Moreover,rollout can use as base policy one of the policies produced by PI,thereby improving on that policy.This is the type of scheme underlying the prominently successful Alpha Zero chess program.In this paper we focus on rollout and PI-like methods for problems where the control consists of multiple components each selected(conceptually)by a separate agent.This is the class of multiagent problems where the agents have a shared objective function,and a shared and perfect state information.Based on a problem reformulation that trades off control space complexity with state space complexity,we develop an approach,whereby at every stage,the agents sequentially(one-at-a-time)execute a local rollout algorithm that uses a base policy,together with some coordinating information from the other agents.The amount of total computation required at every stage grows linearly with the number of agents.By contrast,in the standard rollout algorithm,the amount of total computation grows exponentially with the number of agents.Despite the dramatic reduction in required computation,we show that our multiagent rollout algorithm has the fundamental cost improvement property of standard rollout:it guarantees an improved performance relative to the base policy.We also discuss autonomous multiagent rollout schemes that allow the agents to make decisions autonomously through the use of precomputed signaling information,which is sufficient to maintain the cost improvement property,without any on-line coordination of control selection between the agents.For discounted and other infinite horizon problems,we also consider exact and approximate PI algorithms involving a new type of one-agent-at-a-time policy improvement operation.For one of our PI algorithms,we prove convergence to an agentby-agent optimal policy,thus establishing a connection with the theory of teams.For another PI algorithm,which is executed over a more complex state space,we prove convergence to an optimal policy.Approximate forms of these algorithms are also given,based on the use of policy and value neural networks.These PI algorithms,in both their exact and their approximate form are strictly off-line methods,but they can be used to provide a base policy for use in an on-line multiagent rollout scheme.

Pseudo-Predictor Feedback Control for Multiagent Systems with Both State and Input Delays

This paper is concerned with the consensus problem for high-order continuous-time multiagent systems with both state and input delays.A novel approach referred to as pseudopredictor feedback protocol is proposed.Unlike the predictorbased feedback protocol which utilizes the open-loop dynamics to predict the future states,the pseudo-predictor feedback protocol uses the closed-loop dynamics of the multiagent systems to predict the future agent states.Full-order/reduced-order observer-based pseudo-predictor feedback protocols are proposed,and it is shown that the consensus is achieved and the input delay is compensated by the proposed protocols.Necessary and sufficient conditions guaranteeing the stability of the integral delay systems are provided in terms of the stability of the series of retarded-type time-delay systems.Furthermore,compared with the existing predictor-based protocols,the proposed pseudo-predictor feedback protocol is independent of the input signals of the neighboring agents and is easier to implement.Finally,a numerical example is given to demonstrate the effectiveness of the proposed approaches.

Consensus of multiagent systems based on disturbance observer

The objectives of this work are the development and design of disturbance observers (DO’s) for a team of agents that accomplish consensus on agents’ states in the presence of exogenous disturbances. A pinning control strategy is designed for a part of agents of the multiagent systems without disturbances, and this pinning control can bring multiple agents’ states to reaching an expected consensus value. Under the effect of the disturbances, nonlinear disturbance observers are developed for disturbances generated by an exogenous system to estimate the disturbances. Asymptotical consensus of the multiagent systems with disturbances under the composite controller can be achieved. Finally, by applying an example of multiagent systems with switching topologies and exogenous disturbances, the design of the parameters of DO’s are illuminated.

On consensus protocols for high-order multiagent systems

In this paper,the static consensus problem and the dynamic consensus problem are considered for a class of high-order multiagent systems.With the proposed consensus protocols,necessary and sufficient conditions for the consensus problems are obtained.For the static consensus protocol,the desired consensus speed can be achieved by adjusting feedback gains.Simulations show the effectiveness of the proposed consensus protocols.