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Model Reference Adaptive Control of Five-Phase IPM Motors Based on Neural Network

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2 Author(s)
Lusu Guo ; Dept. of Electr., Comput., & Syst. Eng., Rensselaer Polytech. Inst., Troy, NY, USA ; Parsa, L.

This paper presents a novel model reference adaptive control of five-phase interior-permanent-magnet (IPM) motor drives. The primary controller is designed based on an artificial neural network (ANN) to simulate the nonlinear characteristics of the system without knowledge of accurate motor models 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 operation or above the rated speed with the flux weakening operation. The ANN-based primary controller consists of a radial basis function network which is trained online to adapt system uncertainties. The complete IPM motor drive is simulated in Matlab/Simulink environment and implemented experimentally utilizing a dSPACE DS1104 controller board on a five-phase prototype IPM motor.

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Industrial Electronics, IEEE Transactions on  (Volume:59 ,  Issue: 3 )