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.
@article{arxiv.2112.07779,
title = {Learning Rigidity-based Flocking Control with Gaussian Processes},
author = {Manuela Gamonal and Thomas Beckers and George J. Pappas and Leonardo J. Colombo},
journal= {arXiv preprint arXiv:2112.07779},
year = {2021}
}