Open Access

ARTICLE

A Stable Fuzzy-Based Computational Model and Control for Inductions Motors

Yongqiu Liu¹, Shaohui Zhong^2,*, Nasreen Kausar³, Chunwei Zhang^4,*, Ardashir Mohammadzadeh⁴, Dragan Pamucar^5,6

1 School of Mechanical and Electrical Engineering, Guangdong University of Science & Technology, Dongguan, 523083, China
2 School of Information, Hunan Open University, Changsha, 410081, China
3 Department of Mathematics, Faculty of Arts and Sciences, Yildiz Technical University, Esenler, Istanbul, 34220, Turkey
4 Multidisciplinary Center for Infrastructure Engineering, Shenyang University of Technology, Shenyang, 110870, China
5 Faculty of Organizational Sciences, University of Belgrade, Belgrade, 11000, Serbia
6 College of Engineering, Yuan Ze University, Taoyuan, 320315, Taiwan

* Corresponding Authors: Shaohui Zhong. Email: ; Chunwei Zhang. Email:

(This article belongs to the Special Issue: Advanced Computational Models for Decision-Making of Complex Systems in Engineering)

Computer Modeling in Engineering & Sciences 2024, 138(1), 793-812. https://doi.org/10.32604/cmes.2023.028175

Received 02 December 2022; Accepted 04 April 2023; Issue published 22 September 2023

Abstract

In this paper, a stable and adaptive sliding mode control (SMC) method for induction motors is introduced. Determining the parameters of this system has been one of the existing challenges. To solve this challenge, a new self-tuning type-2 fuzzy neural network calculates and updates the control system parameters with a fast mechanism. According to the dynamic changes of the system, in addition to the parameters of the SMC, the parameters of the type-2 fuzzy neural network are also updated online. The conditions for guaranteeing the convergence and stability of the control system are provided. In the simulation part, in order to test the proposed method, several uncertain models and load torque have been applied. Also, the results have been compared to the SMC based on the type-1 fuzzy system, the traditional SMC, and the PI controller. The average RMSE in different scenarios, for type-2 fuzzy SMC, is 0.0311, for type-1 fuzzy SMC is 0.0497, for traditional SMC is 0.0778, and finally for PI controller is 0.0997.

---

Keywords

---