Neural Network Based Global Adaptive Dynamic Surface Tracking Control for Robot Manipulators

被引：0
|
作者：
Teng, Tao [1 ]
Yang, Chenguang [1 ,2 ]
Xu, Bin [3 ]
Li, Zhijun [1 ]
机构：
[1] South China Univ Technol, Key Lab Autonomous Syst & Networked Control, Coll Automat Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China
[2] Swansea Univ, Zienkiewicz Ctr Computat Engn, Swansea SA1 8EN, W Glam, Wales
[3] Northwestern Polytech Univ, Sch Automat, Xian, Peoples R China
来源：
IEEE ICARM 2016 - 2016 INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS (ICARM) | 2016年
关键词：
STRICT-FEEDBACK SYSTEMS; NONLINEAR-SYSTEMS; NN CONTROL;
D O I：
暂无
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：
A neural network empowered dynamic surface control (DSC) technique is addressed for robot manipulators system with unknown dynamics. In comparison to the conventional adaptive neural control algorithms, which could guarantee semi-globally uniformly ultimate boundedness (SGUUB) only when neural approximation keeps effective, the scheme designed in this paper ensures globally uniformly ultimately bounded (GUUB) stability by integrating a switching mechanism which incorporates an additional robust controller to drag the transient state variables back when they go beyond the neural approximation region. Simulation studies on 2-joint robot manipulator have been carried out to validate the designed controller has excellent performance.
引用
收藏
页码：20 / 25
页数：6
相关论文
共 50 条
  • [21] Neural Network-Based Adaptive Dynamic Surface Control for Permanent Magnet Synchronous Motors
    Yu, Jinpeng
    Shi, Peng
    Dong, Wenjie
    Chen, Bing
    Lin, Chong
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 26 (03) : 640 - 645
  • [22] Neural-Network-Based Adaptive Dynamic Surface Control for MIMO Systems with Unknown Hysteresis
    Liu, Lei
    Wang, Zhanshan
    Shen, Zhengwei
    2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), 2014, : 218 - 223
  • [23] Global adaptive neural dynamic surface control of strict-feedback systems
    Huang, Jeng-Tze
    NEUROCOMPUTING, 2015, 165 : 403 - 413
  • [24] Adaptive fuzzy-neural-network velocity sensorless control for robot manipulator position tracking
    Wai, R. -J.
    Huang, Y. -C.
    Yang, Z. -W.
    Shih, C. -Y.
    IET CONTROL THEORY AND APPLICATIONS, 2010, 4 (06) : 1079 - 1093
  • [25] Adaptive neural network control of robot manipulator using reinforcement learning
    Tang, Li
    Liu, Yan-Jun
    JOURNAL OF VIBRATION AND CONTROL, 2014, 20 (14) : 2162 - 2171
  • [26] Robust Adaptive Neural Network Finite-Time Tracking Control for Robotic Manipulators Without Velocity Measurements
    Zhang, Tie
    Zhang, Aimin
    IEEE ACCESS, 2020, 8 : 126488 - 126495
  • [27] Neural network based dynamic surface integral nonsingular fast terminal sliding mode control for manipulators with disturbance rejection
    Gao, Xunkai
    Yu, Haisheng
    Yang, Qing
    Meng, Xiangxiang
    Zhang, Pengxin
    JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2023, 360 (15): : 11032 - 11054
  • [28] A New Adaptive Neural Network Based Observer for Robotic Manipulators
    Asl, Reza Mohammadi
    Hashemzadeh, Farzad
    Badamchizadeh, Mohammad Ali
    2015 3RD RSI INTERNATIONAL CONFERENCE ON ROBOTICS AND MECHATRONICS (ICROM), 2015, : 663 - 668
  • [29] Practical Time-Delay Control With Adaptive Gains for Trajectory Tracking of Robot Manipulators
    Baek, Jaemin
    Cho, Soonwan
    Han, Soohee
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2018, 65 (07) : 5682 - 5692
  • [30] Neural adaptive dynamic surface control for position tracking of electrohydraulic servo systems based on extended state observer
    Liu, Liyan
    Shen, Gang
    Wang, Wei
    Li, Xiang
    Zhu, Zhencai
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING, 2024, 238 (09) : 1535 - 1555
12345