Online learning-based trajectory tracking for underactuated vehicles with uncertain dynamics

TL;DR

This paper introduces an online learning-based control method for underactuated vehicles that effectively manages unknown dynamics and disturbances, ensuring bounded trajectory tracking errors with high probability.

Contribution

It proposes a novel control law utilizing Gaussian process models as an oracle to predict unknown dynamics in underactuated vehicles.

Findings

Guarantees bounded tracking error with high probability

Demonstrates effectiveness through numerical simulation

Utilizes Gaussian processes for online learning of dynamics

Abstract

Underactuated vehicles have gained much attention in the recent years due to the increasing amount of aerial and underwater vehicles as well as nanosatellites. Trajectory tracking control of these vehicles is a substantial aspect for an increasing range of application domains. However, external disturbances and parts of the internal dynamics are often unknown or very time-consuming to model. To overcome this issue, we present a tracking control law for underactuated rigid-body dynamics using an online learning-based oracle for the prediction of the unknown dynamics. We show that Gaussian process models are of particular interest for the role of the oracle. The presented approach guarantees a bounded tracking error with high probability where the bound is explicitly given. A numerical example highlights the effectiveness of the proposed control law.