An indirect adaptive neuro-fuzzy speed control of induction motors
| Autor: | M. Vahedi, M. Hadad Zarif, A. Akbarzadeh Kalat |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: | |
| Zdroj: | Journal of Artificial Intelligence and Data Mining, Vol 4, Iss 2, Pp 243-251 (2016) |
| Druh dokumentu: | article |
| ISSN: | 2322-5211 2322-4444 |
| DOI: | 10.5829/idosi.JAIDM.2016.04.02.13 |
| Popis: | This paper presents an indirect adaptive system based on neuro-fuzzy approximators for the speed control of induction motors. The uncertainty including parametric variations, the external load disturbance and unmodeled dynamics is estimated and compensated by designing neuro-fuzzy systems. The contribution of this paper is presenting a stability analysis for neuro-fuzzy speed control of induction motors. The online training of the neuro-fuzzy systems is based on the Lyapunov stability analysis and the reconstruction errors of the neuro-fuzzy systems are compensated in order to guarantee the asymptotic convergence of the speed tracking error. Moreover, to improve the control system performance and reduce the chattering, a PI structure is used to produce the input of the neuro-fuzzy systems. Finally, simulation results verify high performance characteristics and robustness of the proposed control system against plant parameter variation, external load and input voltage disturbance. |
| Databáze: | Directory of Open Access Journals |
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