  This paper considers a localized data-driven consensus problem for
leader-follower multi-agent systems with unknown discrete-time agent dynamics,
where each follower computes its local control gain using only their locally
collected state and input data. Both noiseless and noisy data-driven consensus
protocols are presented, which can handle the challenge of the heterogeneity in
control gains caused by the localized data sampling and achieve leader-follower
consensus. The design of these data-driven consensus protocols involves
low-dimensional linear matrix inequalities. In addition, the results are
extended to the case where only the leader's data are collected and exploited.
The effectiveness of the proposed methods is illustrated via simulation
examples.
