Friction prediction of constant tension system based on T-S fuzzy model

LI Yang; BAN Xiao-jun; LU Hong-qian; YIN Hang; HUANG Xian-lin

doi:10.3788/OPE.20172514.0087

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Friction prediction of constant tension system based on T-S fuzzy model

更新时间：2020-08-13

- Friction prediction of constant tension system based on T-S fuzzy model
- Optics and Precision Engineering Vol. 25, Issue 12z, Pages: 87-94(2017)
- 作者机构：
  
  哈尔滨工业大学控制理论与制导技术研究中心,黑龙江哈尔滨,150001
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20172514.0087
  CLC： TH703
- Received：22 August 2017，
  
  Revised：13 September 2017，
  
  Published：31 December 2017
- 稿件说明：
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李杨, 班晓军, 卢鸿谦等. 基于T-S模糊模型的恒张力系统摩擦预测[J]. 光学精密工程, 2017,25(12z): 87-94

LI Yang, BAN Xiao-jun, LU Hong-qian etc. Friction prediction of constant tension system based on T-S fuzzy model[J]. Editorial Office of Optics and Precision Engineering, 2017,25(12z): 87-94
李杨, 班晓军, 卢鸿谦等. 基于T-S模糊模型的恒张力系统摩擦预测[J]. 光学精密工程, 2017,25(12z): 87-94 DOI： 10.3788/OPE.20172514.0087.

LI Yang, BAN Xiao-jun, LU Hong-qian etc. Friction prediction of constant tension system based on T-S fuzzy model[J]. Editorial Office of Optics and Precision Engineering, 2017,25(12z): 87-94 DOI： 10.3788/OPE.20172514.0087.

摘要

吊索恒张力悬挂系统中摩擦力严重影响着系统的精度和性能。为实现对系统中摩擦力的补偿，本文以T-S模糊模型为基础，采用模糊辨识的方法得到系统的模型，从而实现了对摩擦力的预测。采用模糊搜索树法选择了模型的最佳输入变量，引入一种改进的模糊c均值聚类算法提高了系统的辨识效率，并为该算法补充了聚类中心数的选取方法。建立吊索恒张力系统的仿真模型，并以白噪声信号作为输入进行了实验，所得T-S模糊模型输出与仿真输出误差为0.013 5 N，在斜坡、正弦等典型输入信号下，模型输出在1 000 N数量级，而平均误差在0.01 N左右。仿真结果表明，本方法得出的预测模型精度高误差小，是解决恒张力系统摩擦预测问题的有效方法。

Abstract

The friction seriously affects the accuracy and performance of constant tension suspension systems. In order to compensate the friction force

it is necessary to establish the model of the system and forecast the friction. Based on T-S fuzzy model

we use the method of fuzzy identification to obtain the model of the system

thus realizing the prediction of friction force. The optimal input variables of the model are selected by fuzzy search. An improved fuzzy c-means clustering algorithm is used to improve the efficiency of the system

and the method for selection of the clustering center number is added to the algorithm. The white noise signal is employed as input of the constant tension system model in simulations

and the output error of the obtained T-S fuzzy model is 0.013 5 N. In the slope

sine and other typical input signals

the model output is in the order of 1 000 N and the average error is approximately 0.01 N. The simulation results indicate that the proposed model which has high accuracy and low error is an effective method for friction prediction of constant tension systems.

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references

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