Local Bayesian optimization for controller tuning with crash constraints-Reference-Cited by-同舟云学术

Local Bayesian optimization for controller tuning with crash constraints

Published:2024-04-01 Issue:4 Volume:72 Page:281-292
ISSN:0178-2312
Container-title:at - Automatisierungstechnik
language:en
Short-container-title:

Author:

von Rohr Alexander¹^ORCID,Stenger David²^ORCID,Scheurenberg Dominik²^ORCID,Trimpe Sebastian¹^ORCID

Affiliation:

1. 9165 Institute for Data Science in Mechanical Engineering, RWTH Aachen University , Aachen , Germany

2. 9165 Institute of Automatic Control, RWTH Aachen University , Aachen , Germany

Abstract

Abstract 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.

Publisher

Walter de Gruyter GmbH

Link

https://www.degruyter.com/document/doi/10.1515/auto-2023-0181/pdf

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