Model Predictive Control of a Vehicle using Koopman Operator

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00343843" target="_blank" >RIV/68407700:21230/20:00343843 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.ifacol.2020.12.2469" target="_blank" >http://dx.doi.org/10.1016/j.ifacol.2020.12.2469</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ifacol.2020.12.2469" target="_blank" >10.1016/j.ifacol.2020.12.2469</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Model Predictive Control of a Vehicle using Koopman Operator

Popis výsledku v původním jazyce

This paper continues in the work from Cibulka et al. (2019) where a nonlinear vehicle model was approximated in a purely data-driven manner by a linear predictor of higher order, namely the Koopman operator. The vehicle system typically features a lot of nonlinearities such as rigid-body dynamics, coordinate system transformations and most importantly the tire. These nonlinearities are approximated in a predefined subset of the state-space by the linear Koopman operator and used for a linear Model Predictive Control (MPC) design in the highdimension state space where the nonlinear system dynamics evolve linearly. The result is a nonlinear MPC designed by linear methodologies. It is demonstrated that the Koopman-based controller is able to recover from a very unusual state of the vehicle where all the aforementioned nonlinearities are dominant. The controller is compared with a controller based on a classic local linearization and shortcomings of this approach are discussed.

Název v anglickém jazyce

Model Predictive Control of a Vehicle using Koopman Operator

Popis výsledku anglicky

This paper continues in the work from Cibulka et al. (2019) where a nonlinear vehicle model was approximated in a purely data-driven manner by a linear predictor of higher order, namely the Koopman operator. The vehicle system typically features a lot of nonlinearities such as rigid-body dynamics, coordinate system transformations and most importantly the tire. These nonlinearities are approximated in a predefined subset of the state-space by the linear Koopman operator and used for a linear Model Predictive Control (MPC) design in the highdimension state space where the nonlinear system dynamics evolve linearly. The result is a nonlinear MPC designed by linear methodologies. It is demonstrated that the Koopman-based controller is able to recover from a very unusual state of the vehicle where all the aforementioned nonlinearities are dominant. The controller is compared with a controller based on a classic local linearization and shortcomings of this approach are discussed.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the IFAC World Congress 2020

ISBN

—

ISSN

2405-8963

e-ISSN

2405-8963

Počet stran výsledku

6

Strana od-do

4228-4233

Název nakladatele

IFAC

Místo vydání

Laxenburg

Místo konání akce

Berlín

Datum konání akce

11. 7. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000652592500682