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

页码：3468 / 3472

页数：5

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