Introduction to Koopman Operator in Modeling and Control of Dynamic Systems

被引：0
作者：
Kim J.S. [1 ]
Chung C.C. [2 ]
机构：
[1] The Research Institute of Industrial Science, Hanyang University
[2] Department of Electrical Engineering, Hanyang University
来源：
Journal of Institute of Control, Robotics and Systems | 2024年 / 30卷 / 04期
关键词：
Autoencoder; Deep Learning; Kooperman operator; Machine Learning; Modeling and control; Physics-Informed Koopman operator;
D O I：
10.5302/J.ICROS.2024.24.0054
中图分类号：
学科分类号：
摘要：
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. © ICROS 2024.
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收藏
页码：373 / 382
页数：9
相关论文
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