@Article{cmes.2023.028175,
AUTHOR = {Yongqiu Liu, Shaohui Zhong, Nasreen Kausar, Chunwei Zhang, Ardashir Mohammadzadeh, Dragan Pamucar},
TITLE = {A Stable Fuzzy-Based Computational Model and Control for Inductions Motors},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {138},
YEAR = {2024},
NUMBER = {1},
PAGES = {793--812},
URL = {http://www.techscience.com/CMES/v138n1/54264},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2023.028175}
}