  Perfect tracking control for real-world Euler-Lagrange systems is challenging
due to uncertainties in the system model and external disturbances. The
magnitude of the tracking error can be reduced either by increasing the
feedback gains or improving the model of the system. The latter is clearly
preferable as it allows to maintain good tracking performance at low feedback
gains. However, accurate models are often difficult to obtain. In this article,
we address the problem of stable high-performance tracking control for unknown
Euler-Lagrange systems. In particular, we employ Gaussian Process regression to
obtain a data-driven model that is used for the feed-forward compensation of
unknown dynamics of the system. The model fidelity is used to adapt the
feedback gains allowing low feedback gains in state space regions of high model
confidence. The proposed control law guarantees a globally bounded tracking
error with a specific probability. Simulation studies demonstrate the
superiority over state of the art tracking control approaches.
