Deep Dictionary-Free Method for Identifying Linear Model of Nonlinear System with Input Delay

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00385249" target="_blank" >RIV/68407700:21230/25:00385249 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/PC65047.2025.11047273" target="_blank" >http://dx.doi.org/10.1109/PC65047.2025.11047273</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/PC65047.2025.11047273" target="_blank" >10.1109/PC65047.2025.11047273</a>

Result language
angličtina
Original language name
Deep Dictionary-Free Method for Identifying Linear Model of Nonlinear System with Input Delay
Original language description
Nonlinear dynamical systems with input delays pose significant challenges for prediction, estimation, and control due to their inherent complexity and the impact of delays on system behavior. Traditional linear control techniques often fail in these contexts, necessitating innovative approaches. This paper introduces a novel approach to approximate the Koopman operator using an LSTM-enhanced Deep Koopman model, enabling linear representations of nonlinear systems with time delays. By incorporating Long Short-Term Memory (LSTM) layers, the proposed framework captures historical dependencies and efficiently encodes time-delayed system dynamics into a latent space. Unlike traditional extended Dynamic Mode Decomposition (eDMD) approaches that rely on predefined dictionaries, the LSTM-enhanced Deep Koopman model is dictionary-free, which mitigates the problems with the underlying dynamics being known and incorporated into the dictionary. Quantitative comparisons with extended eDMD on a simulated system demonstrate highly significant performance gains in prediction accuracy in cases where the true nonlinear dynamics are unknown and achieve comparable results to eDMD with known dynamics of a system.
Czech name
—
Czech description
—

Project
<a href="/en/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotics and advanced industrial production</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of 25th International Conference on Process Control 2025
ISBN
979-8-3315-2531-6
ISSN
2995-1720
e-ISSN
2995-1739
Number of pages
6
Pages from-to
—
Publisher name
Slovak University of Technology in Bratislava
Place of publication
Bratislava
Event location
Štrbské Pleso
Event date
Jun 3, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001541574400001

Similar results(10)