Velocity-Free Prescribed Performance Control of Dual-Arm Manipulator Based on Neural Networks

被引：0
|
作者：
Zhang, Xiao-Yu [1 ]
Han, Tao [1 ]
Xiao, Bo [1 ]
Yan, Huaicheng [2 ]
机构：
[1] Hubei Normal Univ, Sch Elect Engn & Automat, Huangshi 435002, Hubei, Peoples R China
[2] East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China
来源：
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2024年 / 71卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Force; Manipulators; Observers; Symmetric matrices; Grasping; Target tracking; Mathematical models; Dual-arm manipulator; prescribed-time control; velocity-observer; RBFNN; ADAPTIVE-CONTROL; ROBOT; OBSERVER; SYSTEMS;
D O I：
10.1109/TCSII.2024.3365978
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The prescribed time control of dual-arm manipulator transferring a target without velocity measurement is discussed in this brief. Firstly, a comprehensive dynamic model is formulated using the target position in Cartesian space. Secondly, the prescribed-time tracking problem is simplified to an error constraint problem, where a time regulator is constructed, and a constraint transformation function is established. Thirdly, algorithms for position control are proposed based on radial basis function neural networks (RBFNN), along with the development of comprehensive position/force control, followed by proof of their effectiveness. Finally, the validity of these results is demonstrated through numerical simulations.
引用
收藏
页码：3468 / 3472
页数：5
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