数控机床用永磁直线同步电机干扰力分析及补偿

解红磊; 张为民; 杨勇

数控机床用永磁直线同步电机干扰力分析及补偿

1.
同济大学机械工程学院,上海 201804;
2.
同济大学中德学院,上海 200092

基金项目:

高速/复合数控机床及关键技术创新能力平台(2011ZX04016-021)资助

详细信息

作者简介:
解红磊(1988-)，硕士研究生，研究方向为自适应控制技术、机电-体化，liongleixie@hotmail.com;张为民(联系人)，教授，博士生导师，wmzliang@online.sh.cn

解红磊(1988-)，硕士研究生，研究方向为自适应控制技术、机电-体化，liongleixie@hotmail.com;张为民(联系人)，教授，博士生导师，wmzliang@online.sh.cn

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出版历程
- 收稿日期: 2011-09-11
- 刊出日期: 2015-06-10

Disturbance Force Analysis and Compensation of Permanent Linear Synchronous Motors for NC Machine

1.
College of Mechanical Engineering,Tongji University,Shanghai 201804;
2.
Sino-German College,Tongji University,Shanghai 200092

摘要

摘要: 为提高永磁直线同步电机(PMLSM)的定位精度,采用神经网络技术对以推力波动和切削力为代表的两类干扰力分别进行辨识和补偿。针对PMLSM固定结构引起的推力波动,利用电机空载电流进行辨识和修正,建立推力波动模型并离线优化神经网络权系数。加工过程中,利用神经网络的自适应和非线性能力修正模型误差并补偿第二类干扰力。设计了基于神经网络的三环控制与速度、电流前馈复合控制器,克服了纯模型适应性差的缺点并充分利用可预测干扰力。仿真结果表明该控制器能够有效提高PMLSM定位精度,鲁棒性强。
- 永磁直线同步电机 /
- 推力波动 /
- 神经网络
Abstract: In order to improve the positioning accuracy of the permanent magnet linear synchronous motors(PMLSM),a neural network is adopted to identify and compensate two types of disturbance force represented by thrust ripple and cutting force respectively in this paper.At first,the thrust ripple result from special structure of PMLSM was identified by sampling the no-load current of motor and modifying the phase difference;then,the model of thrust ripple was built and the parameters of neural network offline were optimized.Also,eliminate the error of model and compensate for the second type of disturbance force with the adaptability and nonlinear function of neural network.A complex controller with three loops and feed forward based on neural network is designed to conquer the disadvantage of pure model and makes full use of the predictable disturbance force.Simulation results verify that this controller can improve the positioning accuracy of PMLSM and its robustness is strong.
- PMLSM /
- thrust ripple neural /
- network

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参考文献(12)

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