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
- 김진성; 정정주
- 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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