An indirect adaptive neuro-fuzzy speed control of induction motors
محل انتشار: مجله هوش مصنوعی و داده کاوی، دوره: 4، شماره: 2
سال انتشار: 1395
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 350
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شناسه ملی سند علمی:
JR_JADM-4-2_013
تاریخ نمایه سازی: 19 تیر 1398
چکیده مقاله:
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.
کلیدواژه ها:
نویسندگان
M. Vahedi
Faculty of Electrical & Robotic Engineering, Shahrood University of Technology, Shahrood, Iran.
M. Hadad Zarif
Faculty of Electrical & Robotic Engineering, Shahrood University of Technology, Shahrood, Iran.
A. Akbarzadeh Kalat
Faculty of Electrical & Robotic Engineering, Shahrood University of Technology, Shahrood, Iran.