寻找多智能体系统一致性的迭代学习方法

摘要

本文利用迭代学习的方法研究了带头结点的多智能体系统的一致性问题.文中分别对单积分多智能体系统和一般的线性多智能体系统提出了迭代学习型的一致性算法.该算法对每一个从节点所设计的分布迭代学习序列可以保证从节点能完全跟随上头结点.假设头结点是全局可达的,对于有向拓扑连接图,给出了智能体达到完全一致的充分条件.最后,仿真实例说明了文中所给方法的有效性.%Leader-following multi-agent consensus problems are studied by using the iterative learning control (ILC) approach.The consensus problems of single-integrator and the general linear multi-agent dynamics are considered by the developed scheme,respectively.ILC sequences of individual agents are developed such that they can ensure the follower agents can track the leader perfectly in the finite time interval.Assuming that the leader node is globally reachable,some sufficient conditions to guarantee the multi-agent consensus are derived for the directed communication topologies.Finally,simulation examples are given to illustrate the effectiveness of the proposed methods in this article.