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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotika a pokročilá průmyslová výroba</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of 25th International Conference on Process Control 2025

ISBN

979-8-3315-2531-6

ISSN

2995-1720

e-ISSN

2995-1739

Počet stran výsledku

6

Strana od-do

—

Název nakladatele

Slovak University of Technology in Bratislava

Místo vydání

Bratislava

Místo konání akce

Štrbské Pleso

Datum konání akce

3. 6. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001541574400001