Frequency Adaptive Torque Ripple Suppression for Electrical Drives Using Radial Basis Function Neural Network
Frequency Adaptive Torque Ripple Suppression for Electrical Drives Using Radial Basis Function Neural Network
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DOI：
10.1109/icems59686.2023.10344408
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发表时间：
2023-11
期刊：
影响因子：
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通讯作者：
Yuefei Zuo;Jian An Tan;Chenhao Zhao;Huanzhi Wang;Christopher H. T. Lee;Jun Yang
中科院分区：
文献类型：
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作者：
Yuefei Zuo;Jian An Tan;Chenhao Zhao;Huanzhi Wang;Christopher H. T. Lee;Jun Yang
采用谐振控制器或观测器的电驱动器中的转矩涟漪抑制涉及谐波频率知识。频率锁定环技术可以用来实现频率自适应控制，然而，性能受到系统包含大量谐波时。本文采用径向基函数神经网络（RBFNN）实现电力拖动系统的频率自适应转矩涟漪抑制。RBFNN与自抗扰控制（ADRC）相结合，以提供良好的抑制性能的恒定和涟漪干扰。与基于RBF神经网络的自抗扰控制系统的离线学习不同，该方法可以在线更新权向量，大大提高了系统的鲁棒性和灵活性。通过实验验证了该方法的有效性。
Torque ripple suppression in electric drives employing a resonant controller or observer involves harmonic frequency knowledge. The frequency-locked loop technique can be used to achieve frequency adaptive control, however performance suffers when the system contains numerous harmonics. In this paper, a radial basis function neural network (RBFNN) is used to achieve frequency adaptive torque ripple suppression for electric drives. The RBFNN is combined with active disturbance rejection control (ADRC) to provide good rejection properties for both constant and ripple disturbances. Unlike the ADRC system based on RBFNN with offline learning, the suggested method can update the weights vector online, considerably improving system robustness and flexibility. Various experiments are carried out to validate the effectiveness of the proposed method.