  Controller tuning and parameter optimization are crucial in system design to
improve closed-loop system performance. Bayesian optimization has been
established as an efficient model-free controller tuning and adaptation method.
However, Bayesian optimization methods are computationally expensive and
therefore difficult to use in real-time critical scenarios. In this work, we
propose a real-time purely data-driven, model-free approach for adaptive
control, by online tuning low-level controller parameters. We base our
algorithm on GoOSE, an algorithm for safe and sample-efficient Bayesian
optimization, for handling performance and stability criteria. We introduce
multiple computational and algorithmic modifications for computational
efficiency and parallelization of optimization steps. We further evaluate the
algorithm's performance on a real precision-motion system utilized in
semiconductor industry applications by modifying the payload and reference
stepsize and comparing it to an interpolated constrained optimization-based
baseline approach.
