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동적시스템 모형 및 제어에 있어서 Koopman 연산자 소개Introduction to Koopman Operator in Modeling and Control of Dynamic Systems

Other Titles
Introduction to Koopman Operator in Modeling and Control of Dynamic Systems
Authors
김진성정정주
Issue Date
Apr-2024
Publisher
제어·로봇·시스템학회
Keywords
Kooperman operator; Modeling and control; Physics-Informed Koopman operator; Autoencoder; Machine Learning; Deep Learning; .
Citation
제어.로봇.시스템학회 논문지, v.30, no.4, pp 373 - 382
Pages
10
Indexed
SCOPUS
KCI
Journal Title
제어.로봇.시스템학회 논문지
Volume
30
Number
4
Start Page
373
End Page
382
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/202104
DOI
10.5302/J.ICROS.2024.24.0054
ISSN
1976-5622
2233-4335
Abstract
This paper briefly introduces the Koopman operator framework in the modeling and control of dynamic systems. The paper reviews the theoretical foundations of the Koopman operator, presenting implications for modeling and control in engineering systems. We describe the extended dynamic mode decomposition method to approximate the Koopman operator in a finite-dimensional space. We then show how an autoencoder is obtained for the approximated Koopman operator and analyze the uncertainty quantification. Numerical simulation reveals the validity of the proposed method. We also briefly review the interdisciplinary significance of the physics-informed Koopman operator and its potential to revolutionize the analysis and control of complex dynamic systems across various domains.
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