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Controller tuning and parameter optimization are crucial in system design to improve closed-loop system performance. Bayesian optimization has been established as an efficient model-free controller tuning and adaptation method. However, Bayesian opti
Externí odkaz:
http://arxiv.org/abs/2404.14602
Autor:
Zagorowska, Marta, König, Christopher, Yu, Hanlin, Balta, Efe C., Rupenyan, Alisa, Lygeros, John
Optimization-based controller tuning is challenging because it requires formulating optimization problems explicitly as functions of controller parameters. Safe learning algorithms overcome the challenge by creating surrogate models from measured dat
Externí odkaz:
http://arxiv.org/abs/2310.17431
Autor:
Koenig, Christopher, Ozols, Miks, Makarova, Anastasia, Balta, Efe C., Krause, Andreas, Rupenyan, Alisa
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 adaptat
Externí odkaz:
http://arxiv.org/abs/2306.13479
In robotics, optimizing controller parameters under safety constraints is an important challenge. Safe Bayesian optimization (BO) quantifies uncertainty in the objective and constraints to safely guide exploration in such settings. Hand-designing a s
Externí odkaz:
http://arxiv.org/abs/2210.00762
Adaptive control approaches yield high-performance controllers when a precise system model or suitable parametrizations of the controller are available. Existing data-driven approaches for adaptive control mostly augment standard model-based methods
Externí odkaz:
http://arxiv.org/abs/2101.07825
Autor:
König, Christopher, Khosravi, Mohammad, Maier, Markus, Smith, Roy S., Rupenyan, Alisa, Lygeros, John
This paper presents an automated, model-free, data-driven method for the safe tuning of PID cascade controller gains based on Bayesian optimization. The optimization objective is composed of data-driven performance metrics and modeled using Gaussian
Externí odkaz:
http://arxiv.org/abs/2010.15211
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Akademický článek
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Autor:
Ho, Evelyn Y., Bylund, Carma L., Wollney, Easton, Peterson, Emily B., Wong, Hong-Nei, Koenig, Christopher J.
Publikováno v:
In Patient Education and Counseling December 2021 104(12):2900-2911