Event-Triggered Resilient Consensus of Second-Order Multiagent Networks via Trusted Neighbors

In this brief, we consider the resilient consensus problem of multiagent networks with second-order integrators in the presence of adversarial agents. A novel event-triggered trusted-neighbor subsequence reduction (E-TSR) algorithm is proposed without the f-total/local model assumption. To prevent data pollution, agents filter out any information received from beyond a dynamically determined trusted region established by neighboring trusted agents. Under the event-triggered method, agents make transmissions to neighbors only when necessary in the sense that their values have sufficiently changed since their last transmissions. A necessary and sufficient graph-theoretic condition is obtained to achieve resilient consensus.