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• W2158650012 abstract "This paper presents a novel model reference adaptive control (MRAC) of five-phase interior permanent magnet (IPM) motor drives. The primary controller is designed based on artificial neural network (ANN) to simulate the nonlinear characteristics of the system without knowledge of accurate motor model or parameters. The proposed motor drive decouples the torque and flux components of five-phase IPM motors by applying multiple reference frame transformation. Therefore, the motor can be easily driven below the rated speed with the maximum torque per ampere (MTPA) operation or above the rated speed with the flux weakening operation. The ANN based primary controller consists of a radial basis function (RBF) network which is trained on-line to adapt system uncertainties. The complete IPM motor drive is simulated in Matlab/Simulink environment and implemented experimentally utilizing dSPACE DS1104 DSP board on a five-phase prototype IPM motor." @default.
• W2158650012 created "2016-06-24" @default.
• W2158650012 creator A5076108435 @default.
• W2158650012 creator A5085613281 @default.
• W2158650012 date "2011-05-01" @default.
• W2158650012 modified "2023-10-03" @default.
• W2158650012 title "Model reference adaptive control of five-phase IPM Motors based on neural network" @default.
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• W2158650012 doi "https://doi.org/10.1109/iemdc.2011.5994871" @default.
• W2158650012 hasPublicationYear "2011" @default.
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