Online learning-based trajectory tracking for underactuated vehicles with uncertain dynamics
This addresses trajectory tracking for underactuated vehicles like aerial or underwater systems, but it appears incremental as it builds on existing control and learning methods.
The paper tackles trajectory tracking for underactuated vehicles with uncertain dynamics by developing an online learning-based control law using Gaussian process models as an oracle, guaranteeing a bounded tracking error with high probability and demonstrating effectiveness in a numerical example.
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.