Consensus Theorem for Communication Networks

This lecture covers the consensus theorem for communication networks that are not fully connected, providing examples and summarizing the results discussed so far. The instructor explains the concept of condensation digraphs and consensus, illustrating how states evolve autonomously and influence others over time. The lecture delves into the main result of consensus with GR nodes, emphasizing the importance of primitive and stochastic matrices. Generalizations to multiple subgraphs of leaders are also explored, along with properties of adjacency matrices and initial states. The implications of various consensus properties are discussed, highlighting the interconnected nature of networked control systems.

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