A Dynamic Feedback Neural Model for Identification of the Robot Manipulator

被引：0
作者：
Ay, Mustafa [1 ]
Koca, Gonca Ozmen [1 ]
机构：
[1] Firat Univ, Technol Fac, Mechatron Engn Dept, Elazig, Turkey
来源：
MECHATRONICS 2017: RECENT TECHNOLOGICAL AND SCIENTIFIC ADVANCES | 2018年 / 644卷
关键词：
Robot manipulator; Neural model; Back-propagation; NARX network; Graphical user interface;
D O I：
10.1007/978-3-319-65960-2_43
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：
Robot manipulators are very powerful industrial systems. These systems are used in many different industrial applications. Since the derivation of the mathematical model of a robot manipulator has complex processing load, a suitable neural model can be designed. In this paper, both inverse and forward kinematics equations of a six degree of freedom ( DoF) robot manipulator are given and also adapted to MATLAB environment with a graphical user interface to identify behaviors of the robot manipulator. At the same time, a multilayer artificial neural model is proposed to provide the robot manipulator identification. Back-propagation learning algorithm is used to train dynamic feedback neural model based on NARX network structure. The experimental results are presented to show the performance of the dynamic feedback neural model.
引用
收藏
页码：347 / 355
页数：9
相关论文
共 8 条
  • [1] Alcin OF, 2016, 2016 21ST INTERNATIONAL CONFERENCE ON METHODS AND MODELS IN AUTOMATION AND ROBOTICS (MMAR), P1160, DOI 10.1109/MMAR.2016.7575302
  • [2] Cairl BR, 2015, IEEE INT C INT ROBOT, P6298, DOI 10.1109/IROS.2015.7354276
  • [3] Neural network based inverse kinematics solution for trajectory tracking of a robotic arm
    Duka, Adrian-Vasile
    [J]. 7TH INTERNATIONAL CONFERENCE INTERDISCIPLINARITY IN ENGINEERING (INTER-ENG 2013), 2014, 12 : 20 - 27
  • [4] Gomez S., 2015, INT J MECH MECHATRON, V9, P929
  • [5] Korkmaz D., 2016, INT ENG SCI ED C DEC, P209
  • [6] Ngoc Son N., 2016, INT J ADV ROBOT SYST, V14, P1
  • [7] Putra RY, 2016, 2016 INTERNATIONAL SYMPOSIUM ON ELECTRONICS AND SMART DEVICES (ISESD), P153, DOI 10.1109/ISESD.2016.7886710
  • [8] Neural Network-Based Motion Control of Underactuated Wheeled Inverted Pendulum Models
    Yang, Chenguang
    Li, Zhijun
    Cui, Rongxin
    Xu, Bugong
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2014, 25 (11) : 2004 - 2016
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