Controller tuning is crucial for closed-loop performance but often involves manual adjustments. Although Bayesian optimization (BO) has been established as a data-efficient method for automated tuning, applying it to large and high-dimensional search spaces remains challenging. We extend a recently proposed local variant of BO to include crash constraints, where the controller can only be successfully evaluated in an a-priori unknown feasible region. We demonstrate the efficiency of the proposed method through simulations and hardware experiments. Our findings showcase the potential of local BO to enhance controller performance and reduce the time and resources necessary for tuning.
@article{arxiv.2411.16267,
title = {Local Bayesian Optimization for Controller Tuning with Crash Constraints},
author = {Alexander von Rohr and David Stenger and Dominik Scheurenberg and Sebastian Trimpe},
journal= {arXiv preprint arXiv:2411.16267},
year = {2024}
}