Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-29008
Publication type: | Article in scientific journal |
Type of review: | Peer review (publication) |
Title: | Safe risk-averse bayesian optimization for controller tuning |
Authors: | König, Christopher Ozols, Miks Makarova, Anastasia Balta, Efe C. Krause, Andreas Rupenyan-Vasileva, Alisa |
et. al: | No |
DOI: | 10.1109/LRA.2023.3325991 10.21256/zhaw-29008 |
Published in: | IEEE Robotics and Automation Letters |
Issue Date: | 13-Oct-2023 |
Publisher / Ed. Institution: | IEEE |
ISSN: | 2377-3766 2377-3774 |
Language: | English |
Subjects: | Optimization; Bayesian method; Artificial Intelligence; Probabilistic model; Risk-averse Bayesian optimization; Heteroscedastic noise |
Subject (DDC): | 006: Special computer methods |
Abstract: | 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. |
URI: | https://digitalcollection.zhaw.ch/handle/11475/29008 |
Fulltext version: | Accepted version |
License (according to publishing contract): | Licence according to publishing contract |
Departement: | School of Engineering |
Organisational Unit: | Centre for Artificial Intelligence (CAI) |
Appears in collections: | Publikationen School of Engineering |
Files in This Item:
File | Description | Size | Format | |
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2023_Koenig-etal_Safe-risk-averse-bayesian-optimization-for-controller-tuning_IEEE_AAM.pdf | Accepted Version | 5.07 MB | Adobe PDF | View/Open |
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König, C., Ozols, M., Makarova, A., Balta, E. C., Krause, A., & Rupenyan-Vasileva, A. (2023). Safe risk-averse bayesian optimization for controller tuning. IEEE Robotics and Automation Letters. https://doi.org/10.1109/LRA.2023.3325991
König, C. et al. (2023) ‘Safe risk-averse bayesian optimization for controller tuning’, IEEE Robotics and Automation Letters [Preprint]. Available at: https://doi.org/10.1109/LRA.2023.3325991.
C. König, M. Ozols, A. Makarova, E. C. Balta, A. Krause, and A. Rupenyan-Vasileva, “Safe risk-averse bayesian optimization for controller tuning,” IEEE Robotics and Automation Letters, Oct. 2023, doi: 10.1109/LRA.2023.3325991.
KÖNIG, Christopher, Miks OZOLS, Anastasia MAKAROVA, Efe C. BALTA, Andreas KRAUSE und Alisa RUPENYAN-VASILEVA, 2023. Safe risk-averse bayesian optimization for controller tuning. IEEE Robotics and Automation Letters. 13 Oktober 2023. DOI 10.1109/LRA.2023.3325991
König, Christopher, Miks Ozols, Anastasia Makarova, Efe C. Balta, Andreas Krause, and Alisa Rupenyan-Vasileva. 2023. “Safe Risk-Averse Bayesian Optimization for Controller Tuning.” IEEE Robotics and Automation Letters, October. https://doi.org/10.1109/LRA.2023.3325991.
König, Christopher, et al. “Safe Risk-Averse Bayesian Optimization for Controller Tuning.” IEEE Robotics and Automation Letters, Oct. 2023, https://doi.org/10.1109/LRA.2023.3325991.
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