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首页> 外文期刊>IEEE Transactions on Industrial Electronics >A Self-Learning Solution for Torque Ripple Reduction for Nonsinusoidal Permanent-Magnet Motor Drives Based on Artificial Neural Networks
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A Self-Learning Solution for Torque Ripple Reduction for Nonsinusoidal Permanent-Magnet Motor Drives Based on Artificial Neural Networks

机译：基于人工神经网络的非正弦永磁电动机驱动器转矩脉动自学习解决方案

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摘要

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.
机译：本文提出了一种基于人工神经网络的减少永磁非正弦同步电动机转矩脉动的原始方法。计算最佳电流的解决方案是从几何考虑中得出的，并且没有计算步骤，该步骤通常基于拉格朗日优化。这些最佳电流是从两个超平面获得的。本文考虑了反电动势中存在谐波和齿槽转矩。因此，提出了新的控制方案，以得出最佳定子电流，从而精确地给出所需的电磁转矩（或速度）并最小化欧姆损耗。转矩和速度控制方案都集成了两个神经模块，一个专用于最佳电流计算，另一个用于确保通过电压源逆变器生成这些电流。显示了来自实验室原型的仿真和实验结果，证实了所提出的神经方法的有效性。

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