Learning Rigidity-based Flocking Control with Gaussian Processes

Manuela Gamonal; Thomas Beckers; George J. Pappas; Leonardo J. Colombo

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arXiv · 2112.07779

Learning Rigidity-based Flocking Control with Gaussian Processes

Manuela Gamonal·Thomas Beckers·George J. Pappas·Leonardo J. Colombo

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Abstract

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.

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BibTeX RIS

Manuela Gamonal, Thomas Beckers, George J. Pappas, Leonardo J. Colombo. 2021-12-14. Learning Rigidity-based Flocking Control with Gaussian Processes. https://arxiv.org/abs/2112.07779

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