Deep Koopman learning of nonlinear time-varying systems☆

被引：5

作者：

Hao, Wenjian ^{[1
]}

Huang, Bowen ^{[2
]}

Pan, Wei ^{[3
]}

Wu, Di ^{[2
]}

Mou, Shaoshuai ^{[1
]}

机构：

[1] Purdue Univ, Sch Aeronaut & Astronaut, W Lafayette, IN 47907 USA

[2] Pacific Northwest Natl Lab, Richland, WA USA

[3] Univ Manchester, Dept Comp Sci, Manchester, England

来源：

AUTOMATICA | 2024年 / 159卷

关键词：

Deep neural networks; Koopman operator; Nonlinear time -varying systems; DYNAMIC-MODE DECOMPOSITION; OPERATOR;

D O I：

10.1016/j.automatica.2023.111372

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a data-driven approach to approximate the dynamics of a nonlinear time-varying system (NTVS) by a linear time-varying system (LTVS), which results from the Koopman operator and deep neural networks. Analysis of the approximation error between states of the NTVS and the resulting LTVS is presented. Simulations on a representative NTVS show that the proposed method achieves small approximation errors, even when the system changes rapidly. Furthermore, simulations in an example of quadcopters demonstrate the computational efficiency of the proposed approach. (c) 2023 Elsevier Ltd. All rights reserved.

引用

页数：7

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