Deep Koopman Learning of Nonlinear Time-Varying Systems
Systems and Control
2026-03-16 v4 Systems and Control
Abstract
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 is resulted 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.
Keywords
Cite
@article{arxiv.2210.06272,
title = {Deep Koopman Learning of Nonlinear Time-Varying Systems},
author = {Wenjian Hao and Bowen Huang and Wei Pan and Di Wu and Shaoshuai Mou},
journal= {arXiv preprint arXiv:2210.06272},
year = {2026}
}