A Self-Learning Solution for Torque Ripple Reduction for Nonsinusoidal Permanent-Magnet Motor Drives Based on Artificial Neural Networks

作者：Flieller Damien^*; Ngac Ky Nguyen; Wira Patrice; Sturtzer Guy; Abdeslam Djaffar Ould; Merckle Jean

来源：IEEE Transactions on Industrial Electronics, 2014, 61(2): 655-666.

DOI：10.1109/TIE.2013.2257136

• Adaline
• cogging torque
• homopolar current
• neurocontroller
• permanent-magnet synchronous motor (PMSM)
• torque ripple

This paper presents an original method, based on artificial neural networks, to reduce the torque ripple in a permanent-magnet nonsinusoidal synchronous motor. Solutions for calculating optimal currents are deduced from geometrical considerations and without a calculation step, which is generally based on the Lagrange optimization. These optimal currents are obtained from two hyperplanes. This paper takes into account the presence of harmonics in the back-EMF and the cogging torque. New control schemes are thus proposed to derive the optimal stator currents giving exactly the desired electromagnetic torque (or speed) and minimizing the ohmic losses. The torque and the speed control scheme both integrate two neural blocks, one dedicated for optimal-current calculation and the other to ensure the generation of these currents via a voltage source inverter. Simulation and experimental results from a laboratory prototype are shown to confirm the validity of the proposed neural approach.

• 出版日期2014-2