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Abstract

The new control method for Permanent Magnet Synchronous Motor (PMSM) and Brushless DC Motor (BLDCM) is presented. Balance of power in three-phase permanent magnet synchronous motor is based on conservation of energy law. Space vector theory determined by instantaneous value of phase quantities is applied in mathematical analysis. It makes possible to estimate instantaneous values of reactive energy and electromagnetic torque. The presented control method belongs to flux-oriented method; it synchronizes current vector in relation to stator flux vector. New structure of control system as well as block diagram containing all basic elements and operating modes of specific blocks are described. Simulation studies and experimental results for two kinds of motors: PMSM and BLDCM were performed based on the dSPACE development DS1103 system.
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Abstract

The aim of the studywas to find an effective method of ripple torque compensation for a direct drive with a permanent magnet synchronous motor (PMSM) without time-consuming drive identification. The main objective of the research on the development of a methodology for the proper teaching a neural network was achieved by the use of iterative learning control (ILC), correct estimation of torque and spline interpolation. The paper presents the structure of the drive system and the method of its tuning in order to reduce the torque ripple, which has a significant effect on the uneven speed of the servo drive. The proposed structure of the PMSM in the dq axis is equipped with a neural compensator. The introduced iterative learning control was based on the estimation of the ripple torque and spline interpolation. The structurewas analyzed and verified by simulation and experimental tests. The elaborated structure of the drive system and method of its tuning can be easily used by applying a microprocessor system available now on the market. The proposed control solution can be made without time-consuming drive identification, which can have a great practical advantage. The article presents a new approach to proper neural network training in cooperation with iterative learning for repetitive motion systems without time-consuming identification of the motor.
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