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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Deep learning techniques for model reference adaptive control and identification of complex systems
Original language description
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.
Czech name
—
Czech description
—

Project
<a href="/en/project/EF18_069%2F0009855" target="_blank" >EF18_069/0009855: Electrical Engineering Technologies with High-Level of Embedded Intelligence</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 2020 19th International Conference on Mechatronics - Mechatronika (ME 2020)
ISBN
978-1-72815-602-6
ISSN
—
e-ISSN
—
Number of pages
7
Pages from-to
147-153
Publisher name
IEEE
Place of publication
Piscataway
Event location
on-line, Prague, Czech Republic
Event date
Dec 2, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000662155700027

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