An indirect adaptive neuro-fuzzy speed control of induction motors | ||
| Journal of AI and Data Mining | ||
| مقاله 60، دوره 4، شماره 2، مهر 2016، صفحه 243-251 اصل مقاله (905.15 K) | ||
| نوع مقاله: Review Article | ||
| شناسه دیجیتال (DOI): 10.5829/idosi.JAIDM.2016.04.02.13 | ||
| نویسندگان | ||
| M. Vahedi* ؛ M. Hadad Zarif؛ A. Akbarzadeh Kalat | ||
| Faculty of Electrical & Robotic Engineering, Shahrood University of Technology, Shahrood, Iran. | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| indirect adaptive control؛ neuro-fuzzy approximators؛ uncertainty estimation؛ Stability analysis؛ reconstruction error | ||
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آمار تعداد مشاهده مقاله: 3,509 تعداد دریافت فایل اصل مقاله: 3,912 |
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