Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 246410

The Control Based on Lyapunov Adaptation Law to be Improved by RBF Neural Network and Behavioral Cloning


Kulić, Ranka; Vukić, Zoran
The Control Based on Lyapunov Adaptation Law to be Improved by RBF Neural Network and Behavioral Cloning // EUROCON 2005 : The International Conference on Computer as a Tool : Proceedings of IEEE Region 8 EUROCON 2005 Conference / Milić, Ljiljana (ur.).
Beograd: Academic Mind, 2005. str. 306-309 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 246410 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
The Control Based on Lyapunov Adaptation Law to be Improved by RBF Neural Network and Behavioral Cloning

Autori
Kulić, Ranka ; Vukić, Zoran

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
EUROCON 2005 : The International Conference on Computer as a Tool : Proceedings of IEEE Region 8 EUROCON 2005 Conference / Milić, Ljiljana - Beograd : Academic Mind, 2005, 306-309

ISBN
1-4244-0050-3

Skup
EUROCON 2005 : The International Conference on Computer as a Tool

Mjesto i datum
Beograd, Srbija i Crna Gora, 21-24.11.2005

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
adaptive control; behavioral cloning; RBF

Sažetak
Development of adaptive control system for over its history has a great number of approaches. A crucial property of all approaches is that they have the ability to adapt the controller to accommodate changes in the process. That means that the controller maintains a required level of performance in spite of noise and fluctuation in the process. The paper focuses on model reference adaptive controllers based on Lyapunov adaptive law. The named adaptive controller in some cases has a little level of performance regarding conventional controller. In order to improve the controller performance behavioral cloning and RBF neural network are used. In the behavioral cloning, the system learns from control traces of a human operator. The advantage of the named approach lies in simplicity with regard to the controller parameters tune up in order to reach an appropriate performance

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika



POVEZANOST RADA


Projekti:
0036010

Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Zoran Vukić (autor)

Citiraj ovu publikaciju

Kulić, Ranka; Vukić, Zoran
The Control Based on Lyapunov Adaptation Law to be Improved by RBF Neural Network and Behavioral Cloning // EUROCON 2005 : The International Conference on Computer as a Tool : Proceedings of IEEE Region 8 EUROCON 2005 Conference / Milić, Ljiljana (ur.).
Beograd: Academic Mind, 2005. str. 306-309 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Kulić, R. & Vukić, Z. (2005) The Control Based on Lyapunov Adaptation Law to be Improved by RBF Neural Network and Behavioral Cloning. U: Milić, L. (ur.)EUROCON 2005 : The International Conference on Computer as a Tool : Proceedings of IEEE Region 8 EUROCON 2005 Conference.
@article{article, editor = {Mili\'{c}, L.}, year = {2005}, pages = {306-309}, keywords = {adaptive control, behavioral cloning, RBF}, isbn = {1-4244-0050-3}, title = {The Control Based on Lyapunov Adaptation Law to be Improved by RBF Neural Network and Behavioral Cloning}, keyword = {adaptive control, behavioral cloning, RBF}, publisher = {Academic Mind}, publisherplace = {Beograd, Srbija i Crna Gora} }
@article{article, editor = {Mili\'{c}, L.}, year = {2005}, pages = {306-309}, keywords = {adaptive control, behavioral cloning, RBF}, isbn = {1-4244-0050-3}, title = {The Control Based on Lyapunov Adaptation Law to be Improved by RBF Neural Network and Behavioral Cloning}, keyword = {adaptive control, behavioral cloning, RBF}, publisher = {Academic Mind}, publisherplace = {Beograd, Srbija i Crna Gora} }




Contrast
Increase Font
Decrease Font
Dyslexic Font