Abstract
For multiagent networks described by undirected connectivity graphs, the problem of optimal distributed consensus without prior knowledge of global connectivity is considered. The problem is formulated as a decentralized linear quadratic game, and a linear dynamic feedback scheme that couples the tasks of learning the network topology and driving the network state is shown to solve the game and achieve a Nash equilibrium. This solution results in finite-time consensus in minimum time, and optimizes the transient behavior on the way to consensus with respect to a quadratic global performance measure.
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