Average consensus on general strongly connected digraphs
Average consensus on general strongly connected digraphs
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DOI：
10.1016/j.automatica.2012.08.003
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发表时间：
2012-03
期刊：
影响因子：
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通讯作者：
Kai Cai;H. Ishii
中科院分区：
文献类型：
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作者：
Kai Cai;H. Ishii
我们研究具有单向信息流的一般网络拓扑的多智能体系统的平均共识问题。我们提出了两种线性分布式算法：确定性算法和八卦算法，分别针对代理间通信同步和异步的情况。在这两种情况下，所开发的算法保证了任意强连通有向图的状态平均；特别是，这种图形条件不要求网络是平衡或对称的，从而扩展了文献中先前的结果。我们方法的关键新颖之处在于为每个代理增加一个额外的变量，称为“剩余”，其功能是在本地记录各个状态更新。对于收敛性分析，我们采用图论和非负矩阵工具，加上特征值摄动理论发挥着至关重要的作用。
We study the average consensus problem of multi-agent systems for general network topologies with unidirectional information flow. We propose two linear distributed algorithms, deterministic and gossip, respectively for the cases where the inter-agent communication is synchronous and asynchronous. In both cases, the developed algorithms guarantee state averaging on arbitrary strongly connected digraphs; in particular, this graphical condition does not require that the network be balanced or symmetric, thereby extending previous results in the literature. The key novelty of our approach is to augment an additional variable for each agent, called “surplus”, whose function is to locally record individual state updates. For convergence analysis, we employ graph-theoretic and nonnegative matrix tools, plus the eigenvalue perturbation theory playing a crucial role.