直流电机的自适应神经控制

I. Baruch, R. Garrido, J. Flores, J.C. Martinez
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引用次数: 10

摘要

将递归可训练神经网络(RTNN)与反向传播随时间学习算法相结合,用于直流电机驱动的实时识别和自适应控制。本文提出在系统辨识、状态反馈控制和前馈控制部分分别使用三个rtnn。应用的RTNN模型由于其Jordan规范结构而具有最小数量的参数,这允许将生成的状态向量直接用于直流电机反馈控制。实验结果证实了所描述的识别和控制方法在实践中的适用性,也证实了RTNN的良好质量。
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An adaptive neural control of a DC motor
A recurrent trainable neural network (RTNN) together with a backpropagation trough-time learning algorithm are applied for a real-time identification and adaptive control of a DC-motor drive. The paper proposes to use three RTNNs separately for the parts of the systems identification, the state feedback control and the feedforward control. The applied RTNN model has a minimum number of parameters due to its Jordan canonical structure, which permits to use the generated vector of states directly for a DC-motor feedback control. The experimental results, confirm the applicability of the described identification and control methodology in practice and also confirm the good quality of the RTNN.
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