Adaptive Neural Network Force Tracking Control of Flexible Joint Robot with an Uncertain Environment

被引：10
|
作者：
Yu, Xinbo [1 ,2 ,3 ]
Liu, Sisi [1 ,2 ,3 ]
Zhang, Shuang [1 ,2 ,3 ]
He, Wei [1 ,2 ,3 ]
Huang, Haifeng [1 ,2 ,3 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Intelligence Sci & Technol, Beijing Adv Innovat Ctr Mat Genome Engn, Beijing 100083, Peoples R China
[2] Univ Sci & Technol Beijing, Key Lab Intelligent Bion Unmanned Syst, Minist Educ, Beijing 100083, Peoples R China
[3] Liaoning Acad Mat, Inst Mat Intelligent Technol, Shenyang 110004, Peoples R China
来源：
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2024年 / 71卷 / 06期
基金：
中国博士后科学基金; 中国国家自然科学基金; 北京市自然科学基金;
关键词：
Adaptive force tracking control; Flexible joint robot; Singular perturbation; Neural networks control; SYSTEM; MANIPULATOR; FRAMEWORK; DESIGN;
D O I：
10.1109/TIE.2023.3290250
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a control scheme of the flexible joint robot contacting with an unknown environment is proposed to realize force tracking. Tracking performance is ensured by designing the force-based outer loop and the position-based inner loop of the controller. The reference trajectory is obtained from the outer loop based on interaction force error and the estimated environment stiffness. The inner loop controller of the flexible joint robot based on the singular perturbation method is designed to achieve precise position tracking performance, and neural network is utilized to compensate for uncertainties in robotic dynamics. The stability of the control system is strictly proven by the Lyapunov method. The effectiveness of the proposed method is verified by simulations and experiments on the flexible joint robot.
引用
收藏
页码：5941 / 5949
页数：9
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