Iterative learning control in high-performance motion systems: From theory to implementation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F19%3A43956325" target="_blank" >RIV/49777513:23520/19:43956325 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ETFA.2019.8868996" target="_blank" >http://dx.doi.org/10.1109/ETFA.2019.8868996</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ETFA.2019.8868996" target="_blank" >10.1109/ETFA.2019.8868996</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Iterative learning control in high-performance motion systems: From theory to implementation

Popis výsledku v původním jazyce

Iterative learning control (ILC) enables a perfect compensation for systems that perform the same task over and over again. The aim of this paper is to demonstrate practical applicability of two various state-of-the-art ILC algorithms to point-to-point positioning systems. A simple Frequency domain ILC approach is exploited focusing on systems with exactly repeating motion tasks. Furthermore, flexible ILC is employed to enable learning also for non-repeating tasks. Particular steps providing a seamless transfer from theory and algorithms to practical implementation in a real-time environment by means of industrial-grade SW and HW are given. They may serve as a practical example of a workflow suitable for a wide range of motion control applications. Potential benefits of the learning-type control in comparison with conventional feedback and feedforward control are discussed as well.

Název v anglickém jazyce

Iterative learning control in high-performance motion systems: From theory to implementation

Popis výsledku anglicky

Iterative learning control (ILC) enables a perfect compensation for systems that perform the same task over and over again. The aim of this paper is to demonstrate practical applicability of two various state-of-the-art ILC algorithms to point-to-point positioning systems. A simple Frequency domain ILC approach is exploited focusing on systems with exactly repeating motion tasks. Furthermore, flexible ILC is employed to enable learning also for non-repeating tasks. Particular steps providing a seamless transfer from theory and algorithms to practical implementation in a real-time environment by means of industrial-grade SW and HW are given. They may serve as a practical example of a workflow suitable for a wide range of motion control applications. Potential benefits of the learning-type control in comparison with conventional feedback and feedforward control are discussed as well.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20204 - Robotics and automatic control

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)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings 2019 24th IEEE InternationalConference on Emerging Technologiesand Factory Automation (ETFA)

ISBN

978-1-72810-303-7

ISSN

1946-0740

e-ISSN

1946-0759

Počet stran výsledku

6

Strana od-do

851-856

Název nakladatele

University of Zaragoza

Místo vydání

Zaragoza

Místo konání akce

Zaragoza, Spain

Datum konání akce

10. 9. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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