Parameter estimation and identification of nonlinear systems with the Koopman Operator

Alexandre Mauroy, Jorge Goncalves

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Résumé

We report on a novel framework for parameter estimation and nonlinear system identification. This framework is based ont he key idea that nonlinear system identification in the state space is equivalent to linear identification of the Koopman operator in the space of observables. The proposed technique is divided into three steps. First, data is lifted in a higher dimensional space spanned by properly chosen basis functions. Second, a matrix representation of the Koopman operator is identified in this lifted space. This is aquivalent to a linear identification problem. Finally, the infinitesimal generator of the Koopman semigroup is computed and used to identify the unknown vector field. Two methods (main and dual) are proposed in this framework and complemented with theorical convergence results. The main method is applied to parameter estimation in the case of polynominal vector fields and both autonomous and input-outpout systems are considered. It is shown to be efficient with a general class of systems, including chaotic systems, and well suited to low sampling rate datasets. The other (dual) method is well suited to high-dimensional systems and is used in the context of network reconstruction.
langue originaleAnglais
titreThe Koopman Operator in Systems and Control
Sous-titreConcepts, Methodologies, and Applications
EditeurSpringer
Chapitre13
ISBN (Electronique)978-3-030-35713-9
ISBN (imprimé)978-3-030-35712-2
Etat de la publicationPublié - 2020

Série de publications

NomLecture Notes in Control and Information Sciences
EditeurSpringer
Volume484
ISSN (imprimé)0170-8643

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    Mauroy, A., & Goncalves, J. (2020). Parameter estimation and identification of nonlinear systems with the Koopman Operator. Dans The Koopman Operator in Systems and Control: Concepts, Methodologies, and Applications (Lecture Notes in Control and Information Sciences; Vol 484). Springer.