  Controller tuning and parameter optimization are crucial in system design to
improve both the controller and underlying system performance. Bayesian
optimization has been established as an efficient model-free method for
controller tuning and adaptation. Standard methods, however, are not enough for
high-precision systems to be robust with respect to unknown input-dependent
noise and stable under safety constraints. In this work, we present a novel
data-driven approach, RaGoOSE, for safe controller tuning in the presence of
heteroscedastic noise, combining safe learning with risk-averse Bayesian
optimization. We demonstrate the method for synthetic benchmark and compare its
performance to established BO-based tuning methods. We further evaluate RaGoOSE
performance on a real precision-motion system utilized in semiconductor
industry applications and compare it to the built-in auto-tuning routine.
