具有不确定图拓扑的网络化多智能体系统的控制
Control of Networked Multiagent Systems with Uncertain Graph Topologies
摘要：
多代理系统由代理组成，这些代理通过服从图形拓扑的物理网络在本地交换信息。目前的网络多智能体系统的控制方法假设的图形拓扑结构的知识，以设计分布式控制律，实现所需的全球系统行为。然而，这种假设可能是无效的情况下，图形拓扑结构受到的不确定性，无论是由于物理网络的变化或建模误差的存在，特别是涉及大量的交互代理的多智能体系统。基于这一观点，本文研究了具有不确定图拓扑结构的网络化多智能体系统的分布式控制问题。所提出的框架涉及一个控制器的架构，有能力适应其反馈增益响应系统的变化。具体来说，我们分析表明，所提出的控制器驱动的轨迹的网络多智能体系统的图形拓扑结构与时变的不确定性的一个密切的邻居的轨迹的给定的参考模型具有所需的图形拓扑结构。作为一个特殊的情况下，我们还表明，一个网络化的多智能体系统的图拓扑结构与恒定的不确定性渐近收敛到一个给定的参考模型的轨迹。虽然本文的主要结果是在平均共识问题的背景下，所提出的框架可以用于许多其他问题与网络多智能体系统的不确定图拓扑结构。
英文摘要：
Multiagent systems consist of agents that locally exchange information through a physical network subject to a graph topology. Current control methods for networked multiagent systems assume the knowledge of graph topologies in order to design distributed control laws for achieving desired global system behaviors. However, this assumption may not be valid for situations where graph topologies are subject to uncertainties either due to changes in the physical network or the presence of modeling errors especially for multiagent systems involving a large number of interacting agents. Motivating from this standpoint, this paper studies distributed control of networked multiagent systems with uncertain graph topologies. The proposed framework involves a controller architecture that has an ability to adapt its feed- back gains in response to system variations. Specifically, we analytically show that the proposed controller drives the trajectories of a networked multiagent system subject to a graph topology with time-varying uncertainties to a close neighborhood of the trajectories of a given reference model having a desired graph topology. As a special case, we also show that a networked multi-agent system subject to a graph topology with constant uncertainties asymptotically converges to the trajectories of a given reference model. Although the main result of this paper is presented in the context of average consensus problem, the proposed framework can be used for many other problems related to networked multiagent systems with uncertain graph topologies.
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