Identification and compensation of friction for modular joints based on grey wolf optimizer

Jing-kai CUI; Hua-yang SAI; En-yang ZHANG; Ming-chao ZHU; Zhen-bang XU

doi:10.37188/OPE.20212911.2683

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Identification and compensation of friction for modular joints based on grey wolf optimizer

Information Sciences | 更新时间：2022-01-25

- Identification and compensation of friction for modular joints based on grey wolf optimizer
- Optics and Precision Engineering Vol. 29, Issue 11, Pages: 2683-2691(2021)
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所，吉林长春130033
  2.中国科学院大学，北京 100049
  3.中国科学院大学材料与光电研究中心，北京 100049
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20212911.2683
  CLC： TH117.1
- Received：02 December 2020，
  
  Revised：01 February 2021，
  
  Published：15 November 2021
- 稿件说明：
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崔靖凯,赛华阳,张恩阳等.基于灰狼算法的模块化关节摩擦辨识和补偿[J].光学精密工程,2021,29(11):2683-2691.

CUI Jing-kai,SAI Hua-yang,ZHANG En-yang,et al.Identification and compensation of friction for modular joints based on grey wolf optimizer[J].Optics and Precision Engineering,2021,29(11):2683-2691.
崔靖凯,赛华阳,张恩阳等.基于灰狼算法的模块化关节摩擦辨识和补偿[J].光学精密工程,2021,29(11):2683-2691. DOI： 10.37188/OPE.20212911.2683.

CUI Jing-kai,SAI Hua-yang,ZHANG En-yang,et al.Identification and compensation of friction for modular joints based on grey wolf optimizer[J].Optics and Precision Engineering,2021,29(11):2683-2691. DOI： 10.37188/OPE.20212911.2683.

摘要

针对模块化关节，设计了一套离线辨识方法，对关节的摩擦模型进行了辨识，并对摩擦力进行了补偿。首先，介绍了该模块化关节的结构和控制系统，建立了关节的动力学模型。然后，基于LuGre摩擦模型对关节摩擦力进行了建模，分别用灰狼算法和分段最小二乘配合伪随机序列法辨识了模型的参数，并进行对比分析。最后，设计了基于LuGre摩擦模型的前馈补偿算法，并进行了实验验证。实验结果表明：相较于分段最小二乘法，灰狼算法的辨识精度提高了19.2%；给定幅值为1 （°）/s，频率为10 Hz的正弦速度信号，摩擦补偿使关节速度跟踪误差从0.295 （°）/s减少为0.183 （°）/s；关节速度环带宽从12 Hz提高到18 Hz。多次实验显示，辨识的数据具有较高的重复性，验证了辨识方法的有效性。设计的前馈摩擦补偿算法可以改善关节控制系统的动态性能。

Abstract

To identify the friction model parameters of a modular joint， an off-line identification method that compensates the joint friction is proposed. First， the structure and control system of the modular joint are presented， and the dynamic model of the joint is established. Second， the LuGre friction model is developed. The grey wolf algorithm and piecewise least-square algorithm with a pseudo random sequence are then used to identify the respective model parameters. The results of two methods are compared and analyzed， and a feed-forward compensation algorithm based on the LuGre friction model is designed and verified experimentally. The experimental results indicate that compared with the piecewise least-square method， the identification accuracy of the grey wolf algorithm improved by 19.2%； the joint velocity tracking error decreased from 0.295 （°）/s to 0.183 （°）/s when the given velocity signal was a sine wave with an amplitude of 1 （°）/s and a frequency of 10 Hz； and the velocity loop bandwidth increased from 12 Hz to 18 Hz after friction compensation. Several experiments are repeated， and the identified data exhibit a high repeatability， which verifies the suitability of the proposed method. The proposed feed-forward friction compensation algorithm can be used to improve the dynamic performance of the joint control system.

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references

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