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A new joint friction model for parameter identification and sensor-less hand guiding in industrial robots

Guanghui Liu (Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China)
Qiang Li (Neuroinformatics Group, Center for Cognitive Interaction Technology (CITEC), Bielefeld University, Bielefeld, Germany)
Lijin Fang (Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China)
Bing Han (Key Laboratory of Industrial Control Network and System, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China)
Hualiang Zhang (Key Laboratory of Industrial Control Network and System, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China)

Industrial Robot

ISSN: 0143-991x

Article publication date: 21 July 2020

Issue publication date: 9 October 2020

326

Abstract

Purpose

The purpose of this paper is to propose a new joint friction model, which can accurately model the real friction, especially in cases with sudden changes in the motion direction. The identification and sensor-less control algorithm are investigated to verify the validity of this model.

Design/methodology/approach

The proposed friction model is nonlinear and it considers the angular displacement and angular velocity of the joint as a secondary compensation for identification. In the present study, the authors design a pipeline – including a manually designed excitation trajectory, a weighted least squares algorithm for identifying the dynamic parameters and a hand guiding controller for the arm’s direct teaching.

Findings

Compared with the conventional joint friction model, the proposed method can effectively predict friction factors during the dynamic motion of the arm. Then friction parameters are quantitatively obtained and compared with the proposed friction model and the conventional friction model indirectly. It is found that the average root mean square error of predicted six joints in the proposed method decreases by more than 54%. The arm’s force control with the full torque using the estimated dynamic parameters is qualitatively studied. It is concluded that a light-weight industrial robot can be dragged smoothly by the hand guiding.

Practical implications

In the present study, a systematic pipeline is proposed for identifying and controlling an industrial arm. The whole procedure has been verified in a commercial six DOF industrial arm. Based on the conducted experiment, it is found that the proposed approach is more accurate in comparison with conventional methods. A hand-guiding demo also illustrates that the proposed approach can provide the industrial arm with the full torque compensation. This essential functionality is widely required in many industrial arms such as kinaesthetic teaching.

Originality/value

First, a new friction model is proposed. Based on this model, identifying the dynamic parameter is carried out to obtain a set of model parameters of an industrial arm. Finally, a smooth hand guiding control is demonstrated based on the proposed dynamic model.

Keywords

Acknowledgements

This work was supported by the National Key Research and Development Program of China Under Grant (NO. 2017YFB1301103), National Science and Technology Major Project under Grant (NO. 2017ZX02101007-004), “DEXMAN” project (ID:LI 2811/1-1) funded by DFG, National Natural Science Foundation of China under Grant (NO. 91648204).

Citation

Liu, G., Li, Q., Fang, L., Han, B. and Zhang, H. (2020), "A new joint friction model for parameter identification and sensor-less hand guiding in industrial robots", Industrial Robot, Vol. 47 No. 6, pp. 847-857. https://doi.org/10.1108/IR-03-2020-0053

Publisher

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Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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