工业机器人谐波减速器迟滞特性的神经网络建模

党选举; 王凯利; 姜辉; 伍锡如; 张向文

doi:10.3788/OPE.20192703.0694

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工业机器人谐波减速器迟滞特性的神经网络建模

信息科学 | 更新时间：2020-08-13

- 工业机器人谐波减速器迟滞特性的神经网络建模
- Neural network modeling of hysteresis for harmonic drive in industrial robots
- 光学精密工程 2019年27卷第3期页码：694-701
- 作者机构：
  
  1.桂林电子科技大学电子工程与自动化学院，广西桂林 541004
  2.电子电路国家级实验教学示范中心桂林电子科技大学，广西桂林541004
- 作者简介：
  
  [ "党选举(1965-)，男，陕西武功人，教授，博士生导师，于1986年、1989年毕业于陕西科技大学，分别获学士、硕士学位。于上海交通大学，获得博士学位。主要从事非线性系统建模与控制。E-mail:xjd69@163.com" ]
  [ "王凯利(1992-)，女，河北沙河人，硕士研究生，于2015年毕业于天津工业大学获学士学位。主要从事非线性系统建模与控制。E-mail: 1242561892@qq.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(61263013);国家自然科学基金资助项目(61603107);广西自然科学基金重点项目(2016GXNSFDA380001);广西自然科学基金资助项目(2015GXNSFAA139297)
- DOI：10.3788/OPE.20192703.0694
  中图分类号： TH132.43
- 收稿日期：2018-07-03，
  
  录用日期：2018-9-13，
  
  纸质出版日期：2019-03-15
- 稿件说明：
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党选举, 王凯利, 姜辉, 等. 工业机器人谐波减速器迟滞特性的神经网络建模[J]. 光学精密工程, 2019,27(3):694-701.

Xuan-ju DANG, Kai-li WANG, Hui JIANG, et al. Neural network modeling of hysteresis for harmonic drive in industrial robots[J]. Optics and precision engineering, 2019, 27(3): 694-701.
党选举, 王凯利, 姜辉, 等. 工业机器人谐波减速器迟滞特性的神经网络建模[J]. 光学精密工程, 2019,27(3):694-701. DOI： 10.3788/OPE.20192703.0694.

Xuan-ju DANG, Kai-li WANG, Hui JIANG, et al. Neural network modeling of hysteresis for harmonic drive in industrial robots[J]. Optics and precision engineering, 2019, 27(3): 694-701. DOI： 10.3788/OPE.20192703.0694.

摘要

谐波减速器中柔性环节与传动的非线性摩擦，导致谐波传动出现了不可避免地影响传动精度的复杂迟滞特性，为了描述谐波减速器的迟滞特性，本文构建了一个结构简洁的神经网络迟滞混合模型。该模型由类迟滞特性预处理环节和动态RBF神经网络两部分组成：对输入信号进行类迟滞预处理，处理后的信号与输入信号之间具有类迟滞特性；充分利用动态RBF神经网络实现类迟滞到谐波减速器迟滞特性的高精度映射。根据本文搭建的实验平台，在不同实验条件下获得的数据进行建模验证，在不同频率输入信号、不同负载，实现相同建模精度下

神经网络迟滞混合模型的验证精度为0.449 6(MSE)，远高于经典RBF神经网络模型的3.032 1(MSE)精度，证明了所构造的神经网络迟滞混合模型的有效性和适应性。

Abstract

Nonlinear friction caused by the flexible link and the transmission process in the harmonic drive leads to complex hysteresis characteristics of harmonic transmission that inevitably affect the transmission accuracy. To describe the hysteresis characteristics of the harmonic drive

a concise neural network hysteresis hybrid model

comprising hysteresis-like characteristic preconditioning in series with a dynamic neural network

was presented in this study. It was executed in two steps: the input signal was preprocessed to produce hysteresis-like behavior; the dynamic Radial Basis Function (RBF) neural network was fully utilized to achieve high-precision approximation of hysteresis-like to hysteresis characteristics of the harmonic drive. Moreover

an experimental platform was constructed in this study

and the data obtained under different experimental conditions were modeled and verified. Both at a constant input accuracy and the accuracy with different input signals and loads

the verification accuracy obtained by the neural network hysteresis hybrid model is 0.449 6 (Mean Square Error (MSE))

which is much higher than the 3.0321 (MSE) accuracy of the classical neural network model. This proves the effectiveness and adaptability of the proposed neural network hysteresis hybrid model.

关键词

Keywords

references

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