Abstract
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.
Highlights
In the last few decades, the speed control of induction motors (IMs) has been the focus of widespread researches [1,2,3,4,5,6]
In order to guarantee the asymptotic convergence of the speed tracking error and improve the control system performance, the reconstruction errors of neuro-fuzzy systems have been compensated using a robustifying term in the control law
Speed control of induction motors is very important in many industrial applications such as pump actuators, milling machines and elevators
Summary
In the last few decades, the speed control of induction motors (IMs) has been the focus of widespread researches [1,2,3,4,5,6]. An adaptive neurofuzzy system is designed to approximate the ideal control law, while in indirect methods, first the unknown nonlinear dynamics of the systems are identified and a control input is generated based on the universal approximation theorem [27]. According to this theorem, neuro fuzzy systems can approximate any nonlinear functions with arbitrary small approximation error. In order to guarantee the asymptotic convergence of the speed tracking error and improve the control system performance, the reconstruction errors of neuro-fuzzy systems have been compensated using a robustifying term in the control law.
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