Název:	Deep learning techniques for model reference adaptive control and identification of complex systems
Autoři:	Jamshidi, Mohammad Talla, Jakub Peroutka, Zdeněk
Citace zdrojového dokumentu:	JAMSHIDI, M. TALLA, J. PEROUTKA, Z.Deep learning techniques for model reference adaptive control and identification of complex systems. In: Proceedings of the 2020 19th International Conference on Mechatronics - Mechatronika (ME 2020). Piscataway: IEEE, 2020. s. 147-153. ISBN 978-1-72815-602-6.
Datum vydání:	2020
Nakladatel:	IEEE
Typ dokumentu:	konferenční příspěvek conferenceObject
URI:	2-s2.0-85099299490 http://hdl.handle.net/11025/42948
ISBN:	978-1-72815-602-6
Klíčová slova v dalším jazyce:	adaptive control;artificial neural networks;deep learning;intelligent control;system identification
Abstrakt v dalším 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.
Práva:	Plný text je přístupný v rámci univerzity přihlášeným uživatelům. © IEEE
Vyskytuje se v kolekcích:	Konferenční příspěvky / Conference Papers (KEV) Konferenční příspěvky / Conference papers (RICE) OBD

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