Abstract
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.
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Douiri, M.R., Ferfra, M., Belghazi, O., Cherkaoui, M. (2014). Auto-Adaptive Neuro-Fuzzy Parameter Regulator for Motor Drive. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds) Nature-Inspired Computation and Machine Learning. MICAI 2014. Lecture Notes in Computer Science(), vol 8857. Springer, Cham. https://doi.org/10.1007/978-3-319-13650-9_11
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DOI: https://doi.org/10.1007/978-3-319-13650-9_11
Publisher Name: Springer, Cham
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