Open Access
| Issue |
E3S Web Conf.
Volume 336, 2022
The International Conference on Energy and Green Computing (ICEGC’2021)
|
|
|---|---|---|
| Article Number | 00007 | |
| Number of page(s) | 6 | |
| DOI | https://doi.org/10.1051/e3sconf/202233600007 | |
| Published online | 17 January 2022 | |
- M. Zegrari, A. Badri et B. Oukarfi, “Identification par la méthode du modèle des paramètres d’une machine à courant continu,” 3rd IEEE International Conference: Sciences of Electronic, Technologies of Information and Télécommunications March 27-31, TUNISIA (2005). [Google Scholar]
- B. Shanmuga nithya, A. Mythile, S. Pavithra, N. Nivetha, “Parameter identification of a DC motor,” International Journal of Scientific & Technology Research volume 9, Issue 02, February (2020). [Google Scholar]
- k. Radojka, A. Sanja, and S. Danilo, “Recursive least squares method in parameters identification of DC motors models,” SER.: ELEC. ENERG. vol. 18, no. 3, 467-478, December (2005). [Google Scholar]
- S. Adewusi, “Modeling and parameter identification of a DC motor using constraint optimization technique,” IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE), Vol. 13, PP 46-56 (2016). [Google Scholar]
- S. S. Saab and R. A. Kaed-Bey, “Parameter Identification of a DC Motor: An Experimental Approach,” IEEE International Conf. on Elec.Circuit and Systems. (ICECS), vol.4, pp.981-984, (2001). [Google Scholar]
- B. Arnaud, “Contributions à l’identification paramétrique de modèles à temps continu : extensions de la méthode à erreur de sortie et développement d’une approche spécifique aux systèmes à boucles imbriquées,” Thèse de doctorat, Institut National Polytechnique de Toulouse (INP Toulouse), Octobre (2010). [Google Scholar]
- S. Weerasooriya and M. A. El-Sharkawi, “Identification and Control of a DC Motor using Back-Propagation Neural Networks,” IEEE Tran. On Engergy Conversion, vol.6, no.4, pp.663-669, (1991). [CrossRef] [Google Scholar]
- S. Pothiya, S. Chanposri, S. Kamsawang and W. Kinares, “Parameter Identification of a DC Motor Using Tabu Search,” KKU Engineering Journal, vol. 30, no.3, pp.173-188, (2003). [Google Scholar]
- S. Udomsuk, K-L. Areerak, K-N. Areerak and A. Srikaew, “Parameters Identification of Separately Excited DC Motor using Adaptive Tabu Search Technique,” international conference on advances in energy engineering. IEEE, p. 48-51, (2010). [Google Scholar]
- M. Lankarany and A. Rezazade, “Parameter Estimation Optimization Based on Genetic Algorithm Applied to DC Motor,” IEEE International Conf. on Electrical Engineering. (ICEE), pp.1-6, (2007). [Google Scholar]
- A. Dupuis, M. Ghribi and A. Kaddouri, “Multiobjective Genetic Estimation of DC Motor Parameters and Load Torque,” IEEE International Conf. on Ind. Tech. (ICIT), pp.1511-1514, (2004). [Google Scholar]
- H. Garnier, M. Mensler et A. Richard, “Continuous-time model identification from sampled data, implementation issues and performance evaluation,” International Journal of Control, 76(13):1337-1357, (2003). [CrossRef] [Google Scholar]
- H. Garnier, M. Gilson, T. Bastogne et H. Zbali, “Contsid : un outil logiciel pour l’identification de modèles paramétriques à temps continu à partir de données expérimentales,” In Journées Identification et Modélisation Expérimentale, (2006). [Google Scholar]
- H. Garnier, M. Gilson et V. Laurain , “The contsid toolbox for matlab : extensions and latest developments,” In 15th IFAC Symposium on System Identification, SYSID, vol. 15 de System Identification, pages 735-740, Saint-Malo, France (2009). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.