Deep learning techniques for model reference adaptive control and identification of complex systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F20%3A43960415" target="_blank" >RIV/49777513:23220/20:43960415 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9286698" target="_blank" >https://ieeexplore.ieee.org/document/9286698</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ME49197.2020.9286698" target="_blank" >10.1109/ME49197.2020.9286698</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep learning techniques for model reference adaptive control and identification of complex systems

Popis výsledku v původním jazyce

Although many mathematical and analytical techniques have been presented to control and identify the dynamic systems, there are vast fields of research needing to be developed and extended through Deep Learning (DL) approaches. In this paper, we try to describe how intelligent controllers can interact under control systems in a unique DL-based package. Despite the fact that conventional techniques have some advantages, such as the appropriate reliability and simple implementation for industrial goals, intelligent methods have potential to solve complex problems and identify nonlinear systems. Hence the concentration of this research is on the use of DL techniques to improve the system identification and control in model reference adaptive controllers. A dataset is also used to validate the responses of the proposed techniques. The simulation results demonstrate that not only are the proposed methods consistently appropriate to control the complex systems but also they have acceptable responses in order to utilize for system identification.

Název v anglickém jazyce

Deep learning techniques for model reference adaptive control and identification of complex systems

Popis výsledku anglicky

Although many mathematical and analytical techniques have been presented to control and identify the dynamic systems, there are vast fields of research needing to be developed and extended through Deep Learning (DL) approaches. In this paper, we try to describe how intelligent controllers can interact under control systems in a unique DL-based package. Despite the fact that conventional techniques have some advantages, such as the appropriate reliability and simple implementation for industrial goals, intelligent methods have potential to solve complex problems and identify nonlinear systems. Hence the concentration of this research is on the use of DL techniques to improve the system identification and control in model reference adaptive controllers. A dataset is also used to validate the responses of the proposed techniques. The simulation results demonstrate that not only are the proposed methods consistently appropriate to control the complex systems but also they have acceptable responses in order to utilize for system identification.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/EF18_069%2F0009855" target="_blank" >EF18_069/0009855: Elektrotechnické technologie s vysokým podílem vestavěné inteligence</a><br>

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

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 2020 19th International Conference on Mechatronics - Mechatronika (ME 2020)

ISBN

978-1-72815-602-6

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

147-153

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

on-line, Prague, Czech Republic

Datum konání akce

2. 12. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000662155700027