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.
@article{arxiv.2306.13479,
title = {Safe Risk-averse Bayesian Optimization for Controller Tuning},
author = {Christopher Koenig and Miks Ozols and Anastasia Makarova and Efe C. Balta and Andreas Krause and Alisa Rupenyan},
journal= {arXiv preprint arXiv:2306.13479},
year = {2023}
}