Application of particle swarm optimisation algorithm in manipulator compliance control

IF 0.5 Q4 ENGINEERING, MULTIDISCIPLINARY International Journal of Computing Science and Mathematics Pub Date : 2023-01-01 DOI:10.1504/ijcsm.2023.133641
Kai Guo, Zhi Bai, Zhilin Ma
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引用次数: 0

Abstract

In this study, the motion model and impedance control model of the 9-DOF manipulator are established, and the impedance parameters in the model are optimised using particle swarm optimisation (PSO) algorithm. The experimental data shows that in the linear motion experiment, the maximum relative oscillation error of each scheme based on fuzzy adaptive proportional, integral and differential (PID) algorithm, offline parameter adjustment method and PSO algorithm on the vertical axis is 3.44%, 6.74% and 5.82% respectively, and the process control time from the contact between the robot and the environment to the contact force stability is 2.57 s, 3.82 s and 2.04 s respectively. The experimental results show that the PSO optimised impedance parameters can significantly improve the compliance control effect of the manipulator.
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粒子群优化算法在机械臂柔顺控制中的应用
建立了九自由度机械臂的运动模型和阻抗控制模型,并利用粒子群优化算法对模型中的阻抗参数进行了优化。实验数据表明,在直线运动实验中,基于模糊自适应比例积分微分(PID)算法、离线参数调整方法和粒子群算法的各方案在纵轴上的最大相对振荡误差分别为3.44%、6.74%和5.82%,从机器人与环境接触到接触力稳定的过程控制时间分别为2.57 s、3.82 s和2.04 s。实验结果表明,PSO优化后的阻抗参数能显著提高机械手的柔度控制效果。
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CiteScore
1.30
自引率
0.00%
发文量
37
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