Adaptive Neural Network Control of a Robotic Manipulator With Time-Varying Output Constraints

被引：297
作者：
He, Wei [1 ,2 ]
Huang, Haifeng [1 ,2 ]
Ge, Shuzhi Sam [3 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
[2] Univ Sci & Technol Beijing, Minist Educ, Key Lab Knowledge Automat Ind Proc, Beijing 100083, Peoples R China
[3] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore
来源：
IEEE TRANSACTIONS ON CYBERNETICS | 2017年 / 47卷 / 10期
基金：
北京市自然科学基金; 中国国家自然科学基金;
关键词：
Adaptive neural network (NN) control; barrier Lyapunov function (BLF); disturbance observer (DO); robotic manipulator; time-varying constraints; BARRIER LYAPUNOV FUNCTIONS; UNCERTAIN NONLINEAR-SYSTEMS; TRACKING CONTROL; DESIGN; STABILITY; DELAYS; ROBUST;
D O I：
10.1109/TCYB.2017.2711961
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The control problem of an uncertain n-degrees of freedom robotic manipulator subjected to time-varying output constraints is investigated in this paper. We describe the rigid robotic manipulator system as a multi-input and multi-output nonlinear system. We devise a disturbance observer to estimate the unknown disturbance from humans and environment. To solve the uncertain problem, a neural network which utilizes a radial basis function is used to estimate the unknown dynamics of the robotic manipulator. An asymmetric barrier Lyapunov function is employed in the process of control design to avert the contravention of the time-varying output constraints. Simulation results validate the validity of the presented control scheme.
引用
收藏
页码：3136 / 3147
页数：12
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