Data-driven identification of vehicle dynamics using Koopman operator

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00332651" target="_blank" >RIV/68407700:21230/19:00332651 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/PC.2019.8815104" target="_blank" >http://dx.doi.org/10.1109/PC.2019.8815104</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/PC.2019.8815104" target="_blank" >10.1109/PC.2019.8815104</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Data-driven identification of vehicle dynamics using Koopman operator

Popis výsledku v původním jazyce

This paper presents the results of identification of vehicle dynamics using the Koopman operator. The basic idea is to transform the state space of a nonlinear system (a car in our case) to a higher-dimensional space, using so-called basis functions, where the system dynamics is linear. The selection of basis functions is crucial and there is no general approach on how to select them, this paper gives some discussion on this topic. Two distinct approaches for selecting the basis functions are presented. The first approach, based on Extended Dynamic Mode Decomposition, relies heavily on expert basis selection and is completely data-driven. The second approach utilizes the knowledge of the nonlinear dynamics, which is used to construct eigenfunctions of the Koopman operator which are known by definition to evolve linearly along the nonlinear system trajectory. The eigenfunctions are then used as basis functions for prediction. Each approach is presented with a numerical example and discussion on the feasibility of the approach for a nonlinear vehicle system.

Název v anglickém jazyce

Data-driven identification of vehicle dynamics using Koopman operator

Popis výsledku anglicky

This paper presents the results of identification of vehicle dynamics using the Koopman operator. The basic idea is to transform the state space of a nonlinear system (a car in our case) to a higher-dimensional space, using so-called basis functions, where the system dynamics is linear. The selection of basis functions is crucial and there is no general approach on how to select them, this paper gives some discussion on this topic. Two distinct approaches for selecting the basis functions are presented. The first approach, based on Extended Dynamic Mode Decomposition, relies heavily on expert basis selection and is completely data-driven. The second approach utilizes the knowledge of the nonlinear dynamics, which is used to construct eigenfunctions of the Koopman operator which are known by definition to evolve linearly along the nonlinear system trajectory. The eigenfunctions are then used as basis functions for prediction. Each approach is presented with a numerical example and discussion on the feasibility of the approach for a nonlinear vehicle system.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/GA19-16772S" target="_blank" >GA19-16772S: Aerodynamická tělesa s aktivně řízeným tvarováním</a><br>

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 22nd International Conference on Process Control

ISBN

978-1-7281-3758-2

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

167-172

Název nakladatele

IEEE

Místo vydání

Piscataway, NJ

Místo konání akce

Štrbské Pleso

Datum konání akce

11. 6. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000539039300030