学术探索
学术期刊
学术作者
新闻热点
数据分析
智能评审

Neural Network Control of a Robotic Manipulator With Input Deadzone and Output Constraint

被引：416
作者：
He, Wei [1 ]
David, Amoateng Ofosu [2 ]
Yin, Zhao [2 ]
Sun, Changyin [1 ]
机构：
[1] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
[2] Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu 611731, Peoples R China
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2016年 / 46卷 / 06期
基金：
中国国家自然科学基金; 国家高技术研究发展计划(863计划);
关键词：
Adaptive control; barrier Lyapunov function; constraints; deadzone; neural networks; robotic manipulator; MULTIPLE MOBILE MANIPULATORS; UNCERTAIN NONLINEAR-SYSTEMS; DYNAMIC SURFACE CONTROL; ADAPTIVE-CONTROL; TRACKING CONTROL; VIBRATION CONTROL; LEARNING CONTROL; DELAY SYSTEMS; COMPENSATION; DESIGN;
D O I：
10.1109/TSMC.2015.2466194
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
080201 [机械制造及其自动化];
摘要：
In this paper, we present adaptive neural network tracking control of a robotic manipulator with input deadzone and output constraint. A barrier Lyapunov function is employed to deal with the output constraints. Adaptive neural networks are used to approximate the deadzone function and the unknown model of the robotic manipulator. Both full state feedback control and output feedback control are considered in this paper. For the output feedback control, the high gain observer is used to estimate unmeasurable states. With the proposed control, the output constraints are not violated, and all the signals of the closed loop system are semi-globally uniformly bounded. The performance of the proposed control is illustrated through simulations.
引用
收藏
页码：759 / 770
页数：12
相关论文
共 70 条
[1]
Andrighetto P. L., 2008, ABCM S SERIES MECHAT, V3, P501
[2]
Deadzone compensation in discrete time using adaptive fuzzy logic [J].
Campos, J ;
Lewis, FL .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 1999, 7 (06) :697-707
[3]
Adaptive Vision and Force Tracking Control for Robots With Constraint Uncertainty [J].
Cheah, Chien Chern ;
Hou, Saing Paul ;
Zhao, Yu ;
Slotine, Jean-Jacques E. .
IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2010, 15 (03) :389-399
[4]
Adaptive Consensus Control for a Class of Nonlinear Multiagent Time-Delay Systems Using Neural Networks [J].
Chen, C. L. Philip ;
Wen, Guo-Xing ;
Liu, Yan-Jun ;
Wang, Fei-Yue .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2014, 25 (06) :1217-1226
[5]
Fuzzy Neural Network-Based Adaptive Control for a Class of Uncertain Nonlinear Stochastic Systems [J].
Chen, C. L. Philip ;
Liu, Yan-Jun ;
Wen, Guo-Xing .
IEEE TRANSACTIONS ON CYBERNETICS, 2014, 44 (05) :583-593
[6]
Robust Adaptive Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems With Input Nonlinearities [J].
Chen, Mou ;
Ge, Shuzhi Sam ;
How, Bernard Voon Ee .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2010, 21 (05) :796-812
[7]
Robust stabilization of nonlinear uncertain plants with backlash or dead zone in the actuator [J].
Corradini, ML ;
Orlando, G .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2002, 10 (01) :158-166
[8]
Adaptive backstepping control of wheeled inverted pendulums models [J].
Cui, Rongxin ;
Guo, Ji ;
Mao, Zhaoyong .
NONLINEAR DYNAMICS, 2015, 79 (01) :501-511
[9]
Leader-follower formation control of underactuated autonomous underwater vehicles [J].
Cui, Rongxin ;
Ge, Shuzhi Sam ;
How, Bernard Voon Ee ;
Choo, Yoo Sang .
OCEAN ENGINEERING, 2010, 37 (17-18) :1491-1502
[10]
Adaptive position force control of robot manipulators without velocity measurements: Theory and experimentation [J].
deQueiroz, MS ;
Hu, J ;
Dawson, DM ;
Burg, T ;
Donepudi, SR .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 1997, 27 (05) :796-809
1234567