Data‐Driven Observer Design for Nonlinear Systems Using Automatic Differentiation

ABSTRACT This contribution discusses a method for approximating the observability canonical form of nonlinear systems, circumventing the need for extensive symbolic computations. Instead, we design a high‐gain observer leveraging neural networks and automatic differentiation. The approach aims to ad...

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Bibliographic Details
Published inProceedings in applied mathematics and mechanics Vol. 25; no. 1
Main Authors Fiedler, Julius, Gerbet, Daniel, Röbenack, Klaus
Format Journal Article
LanguageEnglish
Published 01.03.2025
Online AccessGet full text
ISSN1617-7061
1617-7061
DOI10.1002/pamm.202400115

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