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Impedance control for force tracking of a dual-arm cooperative robot based on particle swarm optimization

Li Li (Department of Electronic Information, Heyuan Technology School, Heyuan, China)
Tong Huang (Department of Electrical and Mechanical Engineering, Jieyang Polytechnic, Jieyang, China)
Chujia Pan (Department of Electronic Information, Heyuan Technology School, Heyuan, China)
J.F. Pan (Department of Automation, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China)
Wenbin Su (Department of Electronic and Information Engineering, Heyuan Polytechnic, Heyuan, China)

Industrial Robot

ISSN: 0143-991X

Article publication date: 18 April 2024

Issue publication date: 9 May 2024

139

Abstract

Purpose

The purpose of this paper aims to investigate the adaptive impedance control and its optimized PSO algorithm for force tracking of a dual-arm cooperative robot. Because the dual-arm robot is directly in contact with external environment, controlling the mutual force between robot and external environment is of great importance. Besides, a high compliance of the robot should be guaranteed.

Design/methodology/approach

An impedance control based on Particle Swarm Optimization (PSO) algorithm is designed to track the mutual force and achieve compliance control of the robot end.

Findings

The experimental results show that the impedance control coefficients can be automatically tuned converged by PSO algorithm.

Originality/value

The system can reach a steady state within 0.03 s with overshoot convergence, and the force fluctuation range at the steady state decreases to about ±0.08 N even under the force mutation condition.

Keywords

Acknowledgements

This work was supported in part by National Natural Science Foundation of China (52277060), and in part by Shenzhen Science and Technology Program (JCYJ20220818100000001, SGDX20230116110009018).

Citation

Li, L., Huang, T., Pan, C., Pan, J.F. and Su, W. (2024), "Impedance control for force tracking of a dual-arm cooperative robot based on particle swarm optimization", Industrial Robot, Vol. 51 No. 3, pp. 436-445. https://doi.org/10.1108/IR-10-2023-0247

Publisher

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Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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