Local Bayesian Optimization for Controller Tuning with Crash Constraints

Autor:	von Rohr, Alexander, Stenger, David, Scheurenberg, Dominik, Trimpe, Sebastian
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Systems and Control Computer Science - Machine Learning
Zdroj:	von Rohr, Alexander, Stenger, David, Scheurenberg, Dominik and Trimpe, Sebastian. "Local Bayesian optimization for controller tuning with crash constraints" at - Automatisierungstechnik, vol. 72, no. 4, 2024, pp. 281-292
Druh dokumentu:	Working Paper
DOI:	10.1515/auto-2023-0181
Popis:	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. Comment: Published in at-Automatisierungstechnik
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.16267 Zobrazit plný text záznamu View this record from Arxiv