Safe learning-based trajectory tracking for underactuated vehicles with partially unknown dynamics

Underactuated vehicles have gained much attention in the recent years due to the increasing amount of aerial and underwater vehicles as well as nanosatellites. The safe 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 safe tracking control law for underactuated vehicles using a learning-based oracle for the prediction of the unknown dynamics. The presented approach guarantees the boundedness of the tracking error with high probability where the bound is explicitly given. With additional assumptions, asymptotic stability is achieved. A simulation with a quadrocopter visualizes the effectiveness of the proposed control law.