Dynamic feedback synthesis for finite-time consensus under unknown directed graphs

The problem of finite-time distributed consensus for multi-agent networks described by fixed directed graphs is considered without assuming the global connectivity graph is known to the agents or a central authority. The problem is formulated as a dynamic game where the agents minimize their own local costs by sequentially learning the network connectivity and generating local decisions. A distributed protocol based on online optimization and linear dynamic feedback is shown to achieve an equilibrium with respect to these local costs and near-optimal finite-time consensus.