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Copyright of Gazi Journal of Engineering Sciences (GJES) / Gazi Mühendislik Bilimleri Dergisi is the property of Gazi Journal of Engineering Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The sensorless vector control of a nonsinusoidal flux-distribution permanent magnet synchronous motor (PMSM) has been performed by a trained artificial neural network (ANN) using flux data obtained from the finite element method (FEM). A more sensitive rotor position has been estimated by using the fluxes of each of the three phases of the PMSM. In the proposed approach, magnet flux of the nonsinusoidal PMSM has been calculated by FEM for every single degree. Rotor position and speed values have been estimated by training an ANN with this information. The experimental results obtained by DS1103 development kit have proved the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, new methods for reducing the torque ripple in permanent magnet synchronous motor are proposed. The methods are based on injection of appropriate current harmonics to motor by using adaptive decision fusion algorithms. Three different methods are used in determining injection of current harmonics based on q‐axis current, q‐axis voltage and reference value of the q‐axis current. In these methods, the decision values are linearly combined with weights, which are updated online according to an active fusion method, based on performing orthogonal projections onto convex sets describing these values. The validity of the proposed methods is tested with experimental studies under different operating conditions. [ABSTRACT FROM AUTHOR]