User: Guest  Login
Document type:
Konferenzbeitrag 
Contribution type:
Textbeitrag / Aufsatz 
Author(s):
Capone, A.; Hirche, S. 
Title:
Interval Observers for a Class of Nonlinear Systems Using Gaussian Process Models 
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 
Keywords:
Gaussian process, data-driven, machine learning, interval observer, nonlinear system 
Dewey Decimal Classification:
000 Informatik, Wissen, Systeme 
Book / Congress title:
2019 18th European Control Conference 
Date of congress:
25-28 June 2019 
Publisher:
IEEE 
Year:
2019 
versions