In this paper, a new adaptive network based fuzzy-inference system (ANFIS) architecture is proposed for rotor position and speed estimation over wide range of speed operation for indirect field orientation controlled induction motor drive. This intelligent approach controller incorporates Sugeno model based fuzzy logic laws with a five-layer artificial neural networks (ANNs) scheme. Moreover, for the proposed neuro-fuzzy controller (NFC) an improved self-tuning method is developed based on the induction motor theory and its high performance requirements. The principal task of the tuning method is to adjust the parameters of the fuzzy logic controller (FLC) in order to minimize the square of the error between actual and reference output. The convergence/divergence of the weights is discussed and investigated by simulation.
CITATION STYLE
Douiri, M. R., Ferfra, M., Belghazi, O., & Cherkaoui, M. (2014). Auto-adaptive neuro-fuzzy parameter regulator for motor drive. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8857, pp. 116–127). Springer Verlag. https://doi.org/10.1007/978-3-319-13650-9_11
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