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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-29008
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dc.contributor.authorKönig, Christopher-
dc.contributor.authorOzols, Miks-
dc.contributor.authorMakarova, Anastasia-
dc.contributor.authorBalta, Efe C.-
dc.contributor.authorKrause, Andreas-
dc.contributor.authorRupenyan-Vasileva, Alisa-
dc.date.accessioned2023-11-01T16:30:22Z-
dc.date.available2023-11-01T16:30:22Z-
dc.date.issued2023-10-13-
dc.identifier.issn2377-3766de_CH
dc.identifier.issn2377-3774de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/29008-
dc.description.abstractController 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.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.relation.ispartofIEEE Robotics and Automation Lettersde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectOptimizationde_CH
dc.subjectBayesian methodde_CH
dc.subjectArtificial Intelligencede_CH
dc.subjectProbabilistic modelde_CH
dc.subjectRisk-averse Bayesian optimizationde_CH
dc.subjectHeteroscedastic noisede_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleSafe risk-averse bayesian optimization for controller tuningde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitCentre for Artificial Intelligence (CAI)de_CH
dc.identifier.doi10.1109/LRA.2023.3325991de_CH
dc.identifier.doi10.21256/zhaw-29008-
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.funding.snf180545de_CH
zhaw.webfeedIndustrial Artificial Intelligencede_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen School of Engineering

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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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