Distributed Neighbor Selection for Second-order Semi-Autonomous Networks

This paper examines the distributed neighbor selection problem for second-order semi-autonomous multi-agent networks. By inheriting the leader-to-follower reachability property encoded in the eigenvector associated with the smallest eigenvalue of perturbed graph Laplacian, this paper shows that the convergence rate of a second-order semi-autonomous network can be enhanced on the reduced network constructed by this eigenvector. Moreover, a quantitative connection between the relative rate of change in velocity of neighboring agents and the corresponding entries in this eigenvector is also established, enabling a distributed neighbor selection algorithm for second-order multi-agent networks. The main results in this paper extend our previous work of distributed neighbor selection algorithm design to multi-agent networks with more complicated agent- level dynamics.