  We study the problem of resilient consensus of sampled-data multi-agent
networks with double-integrator dynamics. The term resilient points to
algorithms considering the presence of attacks by faulty/malicious agents in
the network. Each normal agent updates its state based on a predetermined
control law using its neighbors' information which may be delayed while
misbehaving agents make updates arbitrarily and might threaten the consensus
within the network. Assuming that the maximum number of malicious agents in the
system is known, we focus on algorithms where each normal agent ignores large
and small position values among its neighbors to avoid being influenced by
malicious agents. The malicious agents are assumed to be omniscient in that
they know the updating times and delays and can collude with each other. We
deal with both synchronous and partially asynchronous cases with delayed
information and derive topological conditions in terms of graph robustness.
