  Adaptive control approaches yield high-performance controllers when a precise
system model or suitable parametrizations of the controller are available.
Existing data-driven approaches for adaptive control mostly augment standard
model-based methods with additional information about uncertainties in the
dynamics or about disturbances. In this work, we propose a purely data-driven,
model-free approach for adaptive control. Tuning low-level controllers based
solely on system data raises concerns on the underlying algorithm safety and
computational performance. Thus, our approach builds on GoOSE, an algorithm for
safe and sample-efficient Bayesian optimization. We introduce several
computational and algorithmic modifications in GoOSE that enable its practical
use on a rotational motion system. We numerically demonstrate for several types
of disturbances that our approach is sample efficient, outperforms constrained
Bayesian optimization in terms of safety, and achieves the performance optima
computed by grid evaluation. We further demonstrate the proposed adaptive
control approach experimentally on a rotational motion system.
