On robustness and leadership in Markov switching consensus networks

Sarah Huiyi Cen, Vaibhav Srivastava and Naomi Ehrich Leonard

Proceedings of the IEEE Conference on Decision and Control, Melbourne, Australia 2017.

Pdf of paper in conference proceedings
We examine the influence of time-varying interactions, which are modeled by a Markov switching graph (MSG), on noisy multi-agent dynamics. Our focus is on the robustness of both consensus and leader-follower tracking dynamics in the presence of stochastic noise, and we derive expressions for the steady-state covariance of the system’s deviation from consensus and tracking error, respectively. We use these measures to quantify individual and group performance as functions of the interaction graphs and graph switching matrix. We extend notions of robustness and joint centrality indices for static graphs to MSGs.

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