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

Liu Y., Zhong S., Kausar N., Zhang C., Mohammadzadeh A., Pamucar D.

CMES - COMPUTER MODELING IN ENGINEERING AND SCIENCES, cilt.138, sa.1, ss.793-812, 2023 (SCI-Expanded, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 138 Sayı: 1
  • Basım Tarihi: 2023
  • Doi Numarası: 10.32604/cmes.2023.028175
  • Dergi Adı: CMES - COMPUTER MODELING IN ENGINEERING AND SCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.793-812
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

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.