基于动态递归反馈型神经网络的永磁同步电机 转矩观测器设计
基于动态递归反馈型神经网络的永磁同步电机 转矩观测器设计
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深圳市科技创新计划项目(JCYJ20170818164527303, JSGG20180508152228974)

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Design of Torque Observer Based on Dynamic Recursive Feedback Neural Network for Permanent Magnet Synchronous Motor
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Shenzhen Science and Technology Innovation Project(JCYJ20170818164527303, JSGG20180508152228974)

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    摘要:

    现有永磁同步电机普遍存在算法复杂、电机参数辨识困难、电磁转矩难以通过数学模型来精 确估算等问题,从而导致电机控制精度以及驱动系统的整体性能下降。该研究设计了一种基于动态递 归反馈型神经网络的电机电磁转矩网络拓扑模型,使用 MATLAB/Simulink 将该神经网络封装成转矩观 测器,并用于电机转矩的精确估算。实验结果显示,与传统转矩和反向传播神经网络计算方式相比, 该研究所设计的转矩观测器具有更高的转矩计算精度,与反向传播神经网络算法相比具有更高的控制 精度与准确性。

    Abstract:

    For the permanent magnet synchronous motors, the controlling algorithms are usually complex and the motor parameters identification are difficult. Since the electromagnetic torques are difficult to estimate through mathematical models, which leads to a decline in motor control accuracy and overall performance of the drive system. In this paper, a topological model of the electromagnetic torque network of the motor was investigated based on the dynamic recursive feedback (ELMAN) neural network. At the same time, the neural network is built as a torque observer by the MATLAB / Simulink for accurate estimation of the motor torque. In the experiments, traditional torque calculation method and the back propagation neural network are compared with the proposed approach. In comparison, the proposed torque observer has better performance in both torque estimation accuracy and control precision.

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引文格式
闫俞佰,梁嘉宁,郑伟杰,杜帅祥.基于动态递归反馈型神经网络的永磁同步电机 转矩观测器设计 [J].集成技术,2020,9(5):103-113

Citing format
YAN Yubai, LIANG Jianing, ZHENG Weijie, DU Shuaixiang. Design of Torque Observer Based on Dynamic Recursive Feedback Neural Network for Permanent Magnet Synchronous Motor[J]. Journal of Integration Technology,2020,9(5):103-113

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  • 收稿日期:2020-05-16
  • 最后修改日期:2020-08-11
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  • 在线发布日期: 2020-09-23
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