  Flocking control of multi-agents system is challenging for agents with
partially unknown dynamics. This paper proposes an online learning-based
controller to stabilize flocking motion of double-integrator agents with
additional unknown nonlinear dynamics by using Gaussian processes (GP). Agents
interaction is described by a time-invariant infinitesimally minimally rigid
undirected graph. We provide a decentralized control law that exponentially
stabilizes the motion of the agents and captures Reynolds boids motion for
swarms by using GPs as an online learning-based oracle for the prediction of
the unknown dynamics. In particular the presented approach guarantees a
probabilistic bounded tracking error with high probability.
