Online Learning-based Formation Control of Multi-Agent Systems with Gaussian Processes

Abstract

Formation control algorithms for multi-agent systems have gained much attention in the recent years due to the increasing amount of mobile and aerial robotic swarms. The design of safe controllers for these vehicles is a substantial aspect for an increasing range of application domains. However, parts of the vehicle’s dynamics and external disturbances are often unknown or very time-consuming to model. To overcome this issue, we present a formation control law for multi-agent systems based on double integrator dynamics by using Gaussian Processes for online learning of the unknown dynamics. The presented approach guarantees a bounded error to the desired formation with high probability, where the bound is explicitly given. A numerical example highlights the effectiveness of the learning-based formation control law.