在间歇性和异步输出反馈下实现集群网络的一致性
Consensus over Clustered Networks Using Intermittent and Asynchronous Output Feedback
AI总结：
本文提出了一种在间歇性和异步输出反馈下实现集群网络一致性的策略，通过混合系统建模和Lyapunov分析确保一致性集的鲁棒稳定性。
摘要：
分布式一致性协议提供了一种在集群网络中传播信息的机制，允许代理和集群在不需要直接访问整个集合状态的情况下做出决策。在本工作中，我们提出了一种策略，用于在由无向图连接的相同线性时不变系统中实现系统一致性。该无向图的有向子图仅在偶发时间可用。在此工作中，网络中的代理被组织成成对不相交的集群，这会诱导出无向父图的子图。一些集群子图对通过一个集群间子图连接，所有集群和集群间子图的联合构成了无向父图。每个代理使用具有相对于其他代理和集群间异步更新的分布式一致性协议的组件。闭环集合动力学被建模为混合系统，并通过基于Lyapunov的稳定性分析得出使一致性子空间（一致性集）全局指数稳定的充分条件。此外，输入到状态稳定性论证表明，一致性集对一大类扰动具有鲁棒性。为验证目的，提供了一个考虑名义和扰动场景的数值模拟。
英文摘要：
Distributed consensus protocols provide a mechanism for spreading information within clustered networks, allowing agents and clusters to make decisions without requiring direct access to the state of the ensemble. In this work, we propose a strategy for achieving system-wide consensus in the states of identical linear time-invariant systems coupled by an undirected graph whose directed sub-graphs are available only at sporadic times. Within this work, the agents of the network are organized into pairwise disjoint clusters, which induce sub-graphs of the undirected parent graph. Some cluster sub-graph pairs are linked by an inter-cluster sub-graph, where the union of all cluster and inter-cluster sub-graphs yields the undirected parent graph. Each agent utilizes a distributed consensus protocol with components that are updated intermittently and asynchronously with respect to other agents and inter-clusters. The closed-loop ensemble dynamics is modeled as a hybrid system, and a Lyapunov-based stability analysis yields sufficient conditions for rendering the agreement subspace (consensus set) globally exponentially stable. Furthermore, an input-to-state stability argument demonstrates the consensus set is robust to a large class of perturbations. A numerical simulation considering both nominal and perturbed scenarios is provided for validation purposes.
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