Data-Driven Nonlinear Model Reduction Using Koopman Theory: Integrated Control Form and NMPC Case Study-Reference-Cited by-同舟云学术

Data-Driven Nonlinear Model Reduction Using Koopman Theory: Integrated Control Form and NMPC Case Study

Published:2022 Issue: Volume:6 Page:2978-2983
ISSN:2475-1456
Container-title:IEEE Control Systems Letters
language:
Short-container-title:IEEE Control Syst. Lett.

Author:

Schulze Jan C.¹^ORCID,Mitsos Alexander¹^ORCID

Affiliation:

1. Department of Mechanical Engineering, Chair of Process Systems Engineering (AVT.SVT), RWTH Aachen University, Aachen, Germany

Funder

Kopernikus Project SynErgie by the Federal Ministry of Education and Research (BMBF) and project supervision by the Project Management Organization Projektträger Jülich.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Control and Optimization,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7782633/9462165/09790827.pdf?arnumber=9790827

Reference28 articles.

1. Linear identification of nonlinear systems: A lifting technique based on the Koopman operator

2. A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition

3. Deep learning for universal linear embeddings of nonlinear dynamics

4. Koopman operator based observer synthesis for control-affine nonlinear systems

5. Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control

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