温度变化下的伺服系统非线性摩擦建模

谭文斌; 李醒飞; 赵新华; 姚旺; 张晨阳

doi:10.3788/OPE.20142208.2135

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温度变化下的伺服系统非线性摩擦建模

微纳技术与精密机械 | 更新时间：2020-08-13

- 温度变化下的伺服系统非线性摩擦建模
- Nonlinear friction modeling for servo systems in changed temperatures
- 光学精密工程 2014年22卷第8期页码：2135-2141
- 作者机构：
  
  1. 天津大学精密测试技术及仪器国家重点实验室天津,300072
  2. 天津商业大学机械工程学院天津,300131
  3. 天津理工大学机械工程学院天津,300384
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目（No.50975206）()
- DOI：10.3788/OPE.20142208.2135
  中图分类号： TH703;TP273
- 收稿日期：2013-12-11，
  
  修回日期：2014-01-21，
  
  纸质出版日期：2014-08-25
- 稿件说明：
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谭文斌, 李醒飞, 赵新华等. 温度变化下的伺服系统非线性摩擦建模[J]. 光学精密工程, 2014,22(8): 2135-2141

TAN Wen-bin, LI Xing-fei, ZHAO XIN-hua etc. Nonlinear friction modeling for servo systems in changed temperatures[J]. Editorial Office of Optics and Precision Engineering, 2014,22(8): 2135-2141
谭文斌, 李醒飞, 赵新华等. 温度变化下的伺服系统非线性摩擦建模[J]. 光学精密工程, 2014,22(8): 2135-2141 DOI： 10.3788/OPE.20142208.2135.

TAN Wen-bin, LI Xing-fei, ZHAO XIN-hua etc. Nonlinear friction modeling for servo systems in changed temperatures[J]. Editorial Office of Optics and Precision Engineering, 2014,22(8): 2135-2141 DOI： 10.3788/OPE.20142208.2135.

摘要

针对机械伺服系统因温度变化产生的非线性摩擦变化，提出了一种反映温度因素的摩擦建模方法来实现对伺服系统的摩擦补偿。首先，分析了温度和摩擦的关系，并结合修正黏性摩擦的LuGre模型，讨论了该模型各参数与温度之间的关系。利用单隐层BP神经网络描述了随温度变化的各个参数，并确定了神经网络的输入、输出以及传递函数。然后，通过神经网络训练获得神经网络参数，从而得到与温度相关的摩擦模型。最后，改变运行条件，验证了提出的模型对摩擦的估计能力。建立的摩擦模型在不同运行条件、不同温度状态下的最大相对估计偏差小于2.5%，表明其能很好地估计系统摩擦力矩，满足高精度摩擦补偿。

Abstract

In consideration of the nonlinear friction from changed temperatures of a mechanical servo system

a nonlinear friction modeling related to the temperature change was proposed to achieve high precision friction compensation of the servo system. Firstly

the relationship between temperature and friction was analyzed

and the dependence of the parameters on the temperature in the model was further analyzed based on the modified LuGre model of viscous friction. Then

the single hidden layer BP neural network was used to describe the parameters changed with temperature and to determine the input

output parameters and transfer functions of the neural network. Furthermore

an experiment was designed and parameters of the neural network were obtained by training the neural network

by which the friction model related to temperature change was implemented. Finally

the ability of the model for friction estimation was verified by changing operation conditions. The experimental results indicate that the maximum relative estimation error of the frictional model is less than 2.5% when it is applied under different operation and temperature conditions. The friction model related to the temperature change estimates system friction torques in various operating conditions accurately and satisfies the need of high precision friction compensation.

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Keywords

references

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