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
  • [1] Adaptive Trajectory Neural Network Tracking Control for Industrial Robot Manipulators with Deadzone Robust Compensator
    La Van Truong
    Shou Dao Huang
    Vu Thi Yen
    Pham Van Cuong
    INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2020, 18 (09) : 2423 - 2434
  • [2] Adaptive Neural Network Tracking Control of Robotic Manipulators Based on Disturbance Observer
    Li, Tianli
    Zhang, Gang
    Zhang, Tan
    Pan, Jing
    PROCESSES, 2024, 12 (03)
  • [3] Global Adaptive Tracking Control of Robot Manipulators Using Neural Networks with Finite-time Learning Convergence
    Yang, Chenguang
    Teng, Tao
    Xu, Bin
    Li, Zhijun
    Na, Jing
    Su, Chun-Yi
    INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2017, 15 (04) : 1916 - 1924
  • [4] Adaptive Neural Network Tracking Control for Robotic Manipulators With Dead Zone
    Zhou, Qi
    Zhao, Shiyi
    Li, Hongyi
    Lu, Renquan
    Wu, Chengwei
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 30 (12) : 3611 - 3620
  • [5] Adaptive position tracking control system based on recurrent fuzzy wavelet neural networks for robot manipulators
    ThangLong Mai
    Wang, YaoNan
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING, 2014, 228 (07) : 500 - 520
  • [6] Adaptive neural network tracking control of an omnidirectional mobile robot
    Feng, Xingkai
    Wang, Congqing
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING, 2023, 237 (03) : 375 - 387
  • [7] Dynamic Learning From Adaptive Neural Control of Robot Manipulators With Prescribed Performance
    Wang, Min
    Yang, Anle
    IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2017, 47 (08): : 2244 - 2255
  • [8] Adaptive Fault-Tolerant Tracking Control for Multi-Joint Robot Manipulators via Neural Network-Based Synchronization
    Le, Quang Dan
    Yang, Erfu
    SENSORS, 2024, 24 (21)
  • [9] Neural network adaptive control design for robot manipulators under velocity constraints
    Nohooji, Hamed Rahimi
    Howard, Ian
    Cui, Lei
    JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2018, 355 (02): : 693 - 713
  • [10] 3D trajectory tracking control of an underactuated AUV based on adaptive neural network dynamic surface
    Liang, Xiao
    Zhang, Zhao
    Qu, Xingru
    Li, Ye
    Zhang, Rubo
    INTERNATIONAL JOURNAL OF VEHICLE DESIGN, 2020, 84 (1-4) : 203 - 218
12345