Forward-Backward Extended DMD with an Asymptotic Stability Constraint

Autor:	Lortie, Louis, Dahdah, Steven, Forbes, James Richard
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	This paper presents a data-driven method to identify an asymptotically stable Koopman system from noisy data. In particular, the proposed approach combines approximations of the system's forward- and backward-in-time dynamics to reduce bias caused by noisy data while enforcing asymptotic stability. A Koopman model of an inherently asymptotically stable system can be unstable due to noisy data and a poor choice of lifting functions. To prevent identifying an unstable model, the proposed approach imposes an asymptotic stability constraint on the Koopman model. The proposed method is formulated as a semidefinite program and its performance is compared to state-of-the-art methods with a simulated Duffing oscillator dataset and experimental soft robot dataset. Comment: 20 pages, 7 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.10623 Zobrazit plný text záznamu View this record from Arxiv