基于反步神经网络的永磁同步电动机转矩脉动最小化

Z. Xiangdong, Wang Jiang, Li Huiyan
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引用次数: 2

摘要

由于永磁磁场的非线性和定子绕组的变化,电磁转矩在系统中引入波纹,产生转速振荡,使永磁同步电动机性能恶化。在本文中,我们给出了波纹的数值描述,并提出了反步神经网络(NN)控制方案来最小化它们。首先,基于在线权值整定的神经网络利用其逼近特性补偿转矩非线性和外部干扰,使其满足反步的线性参数要求;其次,在永磁同步电机模型的基础上,进行了反推;最后,通过与PID的对比仿真验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Torque ripple minimization in PM synchronous motors based on backstepping neural network
The electromagnetic torque introduces ripples into the system due to the nonlinearity of the permanent magnetic field and the change of the stator winding, which generate speed oscillations and deteriorate the performance of PM synchronous motor (PMSM). In this paper, we have given the numerical description of the ripples and proposed backstepping Neural Network (NN) control scheme to minimize them. First, online weight-tuning based NNs compensate the torque nonlinearity and external disturbances with its approximation characteristic, and make them satisfy the linear parameter requirement of backstepping; Second, backstepping is applied based on the model of PMSM; At last, the comparison simulation with PID are added to demonstrate the effectiveness of the proposed method.
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