An event‐triggered approach to quantized resilient consensus

Abstract

SummaryWe consider an event‐triggered update scheme for the problem of multiagent consensus in the presence of faulty and malicious agents within the network. In particular, we focus on the case where the agents take integer (or quantized) values. To keep the regular agents from being affected by the behavior of faulty agents, algorithms of the mean subsequence reduced type are employed, where neighbors taking extreme values are ignored in the updates. Different from the real‐valued case, the quantized version requires the update rule to be randomized. We characterize the error bound on the achievable level of consensus among the agents as well as the necessary structure for the network in terms of the notion of robust graphs. We verify via a numerical example the effectiveness of the proposed algorithms.