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Titel:

Constrained Bayesian Optimization of a Linear Feed-Forward Controller

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
Rowold, M.; Wischnewski, A.; Lohmann, B.
Seitenangaben Beitrag:
pp. 1-6
Abstract:
Models of dynamic systems often contain uncertain parameters or do not describe all dynamics. In some cases, they cannot represent the plants accurately enough, which limits the achievable performance of model-based controller design. Learning-based controllers can adapt to the true parameters and cope with unmodeled dynamics. However, it must be ensured that an adaption does not cause the system to violate its physical or operating constraints, which could result in dangerous situations or harm...     »
Stichworte:
bayesian optimization; learning algorithms; learning control; linear flters; non-causal flter; ingnon-parametric regression; parameter optimization
Dewey-Dezimalklassifikation:
620 Ingenieurwissenschaften
Kongress- / Buchtitel:
IFAC Workshop on Adaptive and Learning Control Systems, ALCOS [13´th, 2019, Winchester, United Kingdom]
Kongress / Zusatzinformationen:
IFAC-PapersOnLine
Band / Teilband / Volume:
Volume 52, Issue 29
Ausgabe:
Code 141968
Ausrichter der Konferenz:
IFAC
Datum der Konferenz:
4. - 6.12.2019
Verlag / Institution:
Elsevier B.V.
Jahr:
2019
Monat:
Dec
Nachgewiesen in:
Scopus
Serien-ISSN:
24058963
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.1016/j.ifacol.2019.12.612
WWW:
https://www.sciencedirect.com/science/article/pii/S2405896319325546
TUM Einrichtung:
Lehrstuhl für Regelungstechnik
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