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Titel:

Interval Observers for a Class of Nonlinear Systems Using Gaussian Process Models

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
Capone, A.; Hirche, S.
Abstract:
An interval observer design approach for partially unknown nonlinear systems is developed, where the unknown system component is modeled using Gaussian processes and noisy system measurements. The proposed method is applicable for bounded nonlinear systems where the system uncertainty is described by a Lipschitz continuous function. The interval observer generates a correct estimation error with high probability, and the error bound is decreased by employing new training data points
Stichworte:
Gaussian process, data-driven, machine learning, interval observer, nonlinear system
Dewey-Dezimalklassifikation:
000 Informatik, Wissen, Systeme
Kongress- / Buchtitel:
2019 18th European Control Conference
Datum der Konferenz:
25-28 June 2019
Verlag / Institution:
IEEE
Jahr:
2019
Volltext / DOI:
doi: 10.23919/ECC.2019.8795920
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