带时延与丢包的网络化多智能体系统控制器设计

网络时延与数据丢包极大地降低了网络化多智能体系统的控制性能,甚至会破坏稳定性.考虑存在网络时延与数据丢包的网络化多智能体系统,提出了一种基于时延与丢包补偿机制的预测控制器设计方法,来主动地消除网络时延与数据丢包的影响.通过对网络化多智能体预测控制系统的分析,给出了能够保证系统稳定性与一致性的控制器设计步骤.最后的仿真实例表明了该方法的有效性.     

具有不同时延的多智能体系统一致性分析

针对有向加权且存在全局可达点的静态网络拓扑, 考虑了同时具有通信时延和输入时延的一阶、二阶智能体系统的运动一致性问题. 基于广义Nyquist准则与频域控制理论的方法, 分析并得到了网络中所有智能体渐进收敛到一致状态的充分条件. 通过该条件发现一致性的达到只与系统的耦合强度、智能体的输入时延以及各自的连接状态信息有关, 与通信时延无关. 但是, 通信时延的存在却要影响系统的动态特性. 仿真实验结果进一步验证了理论分析所得结论的正确性.     

Controllability of Discrete-Time Multi-Agent Systems with Multiple Leaders on Fixed Networks

This paper investigates controllability of discrete-time multi-agent systems with multiple leaders on fixed networks. The leaders are particular agents playing a part in external inputs to steer other member agents. The followers can arrive at any predetermined configuration by regulating the behaviors of the leaders. Some sufficient and necessary conditions are proposed for the controllability of discrete-time multi-agent systems with multiple leaders. Moreover, the case with isolated agents is discussed. Numerical examples and simulations are proposed to illustrate the theoretical results we established.     

Leader-following consensus criteria for multi-agent systems with time-varying delays and switching interconnection topologies

We consider multi-agent systems with time-varying delays and switching interconnection topologies.By constructing a suitable Lyapunov-Krasovskii functional and using the reciprocally convex approach,new delay-dependent consensus criteria for the systems are established in terms of linear matrix inequalities(LMIs),which can be easily solved by using various effective optimization algorithms.Two numerical examples are given to illustrate the effectiveness of the proposed methods.     

Coordinated Control of Multi-Agent Systems with a Varying-Velocity Leader and Input Saturation

In this paper, we investigate a leader-following tracking problem for multi-agent systems with bounded inputs. We propose a distributed bounded protocol for each follower to track a leader whose states may not be completely measured. We theoretically prove that each agent can follow the leader with estimable track errors. Finally, some numerical simulations are presented to illustrate our theoretical results.     

Sociology and Social Theory in Agent Based Social Simulation: A Symposium

A lengthy and intensive debate about the role of sociology in agent based social simulation dominated the email list simsoc@jiscmail.ac.uk during the autumn of 2000. The debate turned on the importance of models being devised to capture the properties of whole social systems and whether those properties should determine agent behaviour or, conversely, whether the properties of social systems should emerge from the behaviour and interaction of the agents and, if so, how that emergence should be represented. The positions of four of the main protagonists concerned specifically with the modelling issues are reprised and extended in this symposium.     

Experimental Validation of Entropy-Driven Swarm Exploration under Sparsity Constraints with Sparse Bayesian Learning

Increasing the autonomy of multi-agent systems or swarms for exploration missions requires tools for efficient information gathering. This work studies this problem from theoretical and experimental perspectives and evaluates an exploration system for multiple ground robots that cooperatively explore a stationary spatial process. For the distributed model, two conceptually different distribution paradigms are considered. The exploration is based on fusing distributively gathered information using Sparse Bayesian Learning (SBL), which permits representing the spatial process in a compressed manner and thus reduces the model complexity and communication load required for the exploration. An entropy-based exploration criterion is formulated to guide the agents. This criterion uses an estimation of a covariance matrix of the model parameters, which is then quantitatively characterized using a D-optimality criterion. The new sampling locations for the agents are then selected to minimize this criterion. To this end, a distributed optimization of the D-optimality criterion is derived. The proposed entropy-driven exploration is then presented from a system perspective and validated in laboratory experiments with two ground robots. The experiments show that SBL together with the distributed entropy-driven exploration is real-time capable and leads to a better performance with respect to time and accuracy compared with similar state-of-the-art algorithms.     

Utility theory in conflict resolution

In this paper, multi-attribute utility theory is extended to accommodate adversarial problem solving situations involving multiple interacting agents. Such situations are resolved by partial goal satisfaction and persuasion, and have only scantily been described in the AI literature. Utility theory is shown to provide a computational framework to (a) generate a compromise solution that partially satisfies the conflicting goals of the agents, (b) evaluate whether a solution is an improvement on a previously rejected one, and (c) determine the effectiveness of persuasive arguments. Our examples are taken from the domain of labor mediation and are implemented in a computer program, called the PERSUADER.     

基于MAS的智能诊断技术集成方法研究

对于复杂分布式故障诊断的特点,集成多种智能方法求解是一种有效的途径.但如何确定这些诊断方法的相对诊断能力是基于多代理技术的故障诊断系统中的一个关键问题.文章基于问题求解的粒度理论,在对设备故障域进行分解的基础上,提出了基于多代理技术的三层评估模型,不仅能对诊断专家的诊断能力进行评估,而且也能对多诊断专家的协调诊断能力进行评估.该方法简化了问题求解的难度,提高了多专家评价的精度.可广泛应用于多诊断专家代理联盟的创建和诊断结论的融合中.     

Iterative learning consensus control with initial state learning for fractional order distributed parameter models multi-agent systems

This paper considers the consensus control problem of multi-agent systems (MAS) with distributed parameter models. Based on the framework of network topologies, a second-order PI-type iterative learning control (ILC) protocol with initial state learning is proposed by using the nearest neighbor knowledge. A discrete system for proposed ILC is established, and the consensus control problem is then converted to a stability problem for such a discrete system. Furthermore, by using generalized Gronwall inequality, a sufficient condition for the convergence of the consensus errors between any two agents is obtained. Finally, the validity of the proposed method is verified by two numerical examples.