A simple stable adaptive neuro-fuzzy speed controller for induction motors

Article type: Research Article

Authors: Vahedi, Mojtaba^* | Zarif, Mohammad Hadad | Kalat, Ali Akbarzadeh

Affiliations: Department of Electrical and Robotic Engineering, University of Shahrood, Shahrood, Iran

Correspondence: [*] Correspondence to: Mojtaba Vahedi, Department of Electrical and Robotic Engineering, University of Shahrood, Shahrood, P.o. box 36199-95161, Iran. Tel.: +989124734622; vahedi@shahroodut.ac.ir

Abstract: This paper presents a novel stable speed control approach for induction motors (IMs) using approximation capability of neural networks and fuzzy systems. Considering the fact that most of previous works are based on direct torque control (DTC) and field oriented control (FOC) without any stability analysis, the main contribution of this paper is developing a simple speed controller for medium sized IMs with guaranteed stability. The uncertainties including parametric variations, the external load disturbance and unmodeled dynamics is estimated and compensated by designing a neuro-fuzzy controller. The reconstruction error of the neuro-fuzzy estimator is compensated in order to guarantee the asymptotic convergence of the speed tracking error using Barbalat’s lemma. Finally, simulation results show that the proposed controller provides high-performance characteristics and is robust with regard to plant parameter variation, external load and input voltage disturbance.

Keywords: Induction motor, neuro-fuzzy systems, uncertainty estimation and compensation, reconstruction error, Barbalat’s lemma

DOI: 10.3233/IFS-141326

Journal: Journal of Intelligent & Fuzzy Systems, vol. 29, no. 2, pp. 571-581, 2015