基于LSTM与牛顿迭代的两轴系统轮廓误差控制  

Contour error control of two-axis system based on LSTM and Newton iteration

作　　者：黄华[1] 赵秋舸 何再兴[2] 李嘉然 HUANG Hua;ZHAO Qiu-ge;HE Zai-xing;LI Jia-ran(School of Mechanical and Electronical Engineering,Lanzhou University of Technology,Lanzhou 730050,China;School of Mechanical Engineering,Zhejiang University,Hangzhou 310027,China)

机构地区：[1]兰州理工大学机电工程学院,甘肃兰州730050 [2]浙江大学机械工程学院,浙江杭州310027

出　　处：《浙江大学学报（工学版）》2023年第1期10-20,共11页Journal of Zhejiang University：Engineering Science

基　　金：国家自然科学基金资助项目(51965037,51565030)。

摘　　要：针对轮廓误差影响运动系统精度的问题,提出结合长短期记忆神经网络(LSTM)和牛顿迭代法对轮廓误差进行预测、通过转换任务坐标系对轮廓误差进行补偿的方法.在运动平台上提取特征轮廓与数据,将牛顿迭代法应用于对轮廓误差的计算,通过计算出的轮廓误差对优化后的LSTM神经网络进行训练,建立更准确的轮廓误差预测模型.通过转换任务坐标系,将预测的轮廓误差作为前馈补偿到参考轮廓中,提高轮廓控制性能.通过试验对比PID、迭代法和神经网络法,利用随机NRBUS轨迹验证泛化性,表明提出的方法能够有效地预测并控制轮廓误差,在精密运动控制领域有良好的应用前景.An approach of contour error prediction based on long short-term memory neural network(LSTM) and Newton iteration and the contour error compensation by transforming the task coordinate system was proposed in order to address the problem that the accuracy of two-axis motion was affected by contour error. The feature contour and data were extracted from the control system of the two-axis motion platform, and the contour error was obtained by Newton’s method, which was employed as the training data of LSTM neural network. Then a more accurate prediction model of contour error was obtained. The predicted contour error was compensated to the reference contour through feedforward control by transforming the task coordinate system so as to improve the contour control performance. The random NRBUS curve was used to verify its generalization by comparing PID, ILC and neural network. The experimental results show that the proposed approach can effectively predict and control the contour error, and has good potential application value in the precision motion control.

关 键 词：两轴运动控制 轮廓误差 长短期记忆神经网络 前馈补偿 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]