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Titel:

Real-Time Learning of Non-Gaussian Uncertainty Models for Autonomous Racing

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
Wischnewski, A.; Betz, J.; Lohmann, B.
Seitenangaben Beitrag:
pp. 609-615
Abstract:
Performance and robustness targets have been considered for controller design for decades. However, robust controllers usually suffer from performance limitations due to conservative uncertainty assumptions made a priori to system operation. The increased number of systems (e.g. autonomous vehicles) which require high-performance operation in safety- critical environments is motivating research in novel design methods. Recently, machine learning methods have emerged as a promising way to reduce...     »
Stichworte:
Controllers; Embedded systems; Gaussian noise (electronic); probability distributions; racing automobiles; safety engineering
Dewey-Dezimalklassifikation:
620 Ingenieurwissenschaften
Herausgeber:
Institute of Electrical and Electronics Engineers Inc.
Kongress- / Buchtitel:
Proceedings of the IEEE Conference on Decision and Control [59th, 2020, South Korea]
Band / Teilband / Volume:
Vol. 2020-December
Ausrichter der Konferenz:
IEEE
Datum der Konferenz:
14.-18.12.2020
Verlag / Institution:
IEEE
Publikationsdatum:
14.12.2020
Jahr:
2020
Jahr / Monat:
2020-12
Monat:
Dec
Seiten:
pp. 609-615
Nachgewiesen in:
Scopus; Web of Science
Print-ISBN:
978-172817447-1
Serien-ISSN:
0743-1546
Reviewed:
ja
Sprache:
en
Erscheinungsform:
Print
Volltext / DOI:
doi:10.1109/CDC42340.2020.9304230
WWW:
https://ieeexplore.ieee.org/document/9304230
TUM Einrichtung:
Lehrstuhl für Regelungstechnik
Eingabe:
05.02.2021
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