Modeling the Dynamic of SCARA Robot Using Nonlinear Autoregressive Exogenous Input Neural Network Model

被引：0
|
作者：
Rafiei, Hamed [1 ]
Hosseini, Ali Aali [1 ]
Tootoonchi, Alireza Akbarzadeh [1 ]
机构：
[1] Ferdowsi Univ Mashhad, Dept Mech Engn, Dept Elect Engn, Mashhad, Razavi Khorasan, Iran
来源：
26TH IRANIAN CONFERENCE ON ELECTRICAL ENGINEERING (ICEE 2018) | 2018年
关键词：
SCARA Robot; Direct Dynamic; NARX Neural Network; System Identification; NARX;
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：
One of the most useful robots in industrial is SCARA robot. Knowing dynamic of this robot is important for understating behavior of robot and designing a controller. A good way to model dynamic of robot is system identification based on data. In this paper, nonlinear Autoregressive exogenous input (NARX) neural network has been used for modeling and identification the direct dynamic of Ferdowsi university of Mashhad (FUM) SCARA robot. Results show that the proposed method work.
引用
收藏
页码：994 / 999
页数：6
相关论文
共 50 条
  • [1] Gas turbine monitoring using neural network dynamic nonlinear autoregressive with external exogenous input modelling
    Ben Rahmoune, Mohamed
    Hafaifa, Ahmed
    Kouzou, Abdellah
    Chen, XiaoQi
    Chaibet, Ahmed
    [J]. MATHEMATICS AND COMPUTERS IN SIMULATION, 2021, 179 : 23 - 47
  • [2] Predictive modeling of pressurized water reactor transients using nonlinear autoregressive with exogenous input neural network
    Ejigu, Derjew Ayele
    Liu, Xiaojing
    [J]. PROGRESS IN NUCLEAR ENERGY, 2023, 161
  • [3] State of Charge Estimation using a Nonlinear Autoregressive with Exogenous Input Neural Network
    Eleftheriadis, Panagiotis
    Lodigiani, Filippo
    Bruneri, Jacopo
    Ogliari, Emanuele
    Leva, Sonia
    [J]. 2023 IEEE BELGRADE POWERTECH, 2023,
  • [4] Modeling the cardiovascular system using a nonlinear additive autoregressive model with exogenous input
    Riedl, M.
    Suhrbier, A.
    Malberg, H.
    Penzel, T.
    Bretthauer, G.
    Kurths, J.
    Wessel, N.
    [J]. PHYSICAL REVIEW E, 2008, 78 (01)
  • [5] Nonlinear Autoregressive Model With Exogenous Input Recurrent Neural Network to Predict Satellites' Clock Bias
    Liang, Yifeng
    Xu, Jiangning
    Li, Fangneng
    Jiang, Pengfei
    [J]. IEEE ACCESS, 2021, 9 : 24416 - 24424
  • [6] Model Order Reduction of Nonlinear Circuit using Proper Orthogonal Decomposition and Nonlinear Autoregressive with eXogenous input (NARX) Neural Network
    Nagaraj, S.
    Seshachalam, D.
    Hucharaddi, Sunil
    [J]. PROCEEDINGS OF THE 2018 16TH ACM/IEEE INTERNATIONAL CONFERENCE ON FORMAL METHODS AND MODELS FOR SYSTEM DESIGN (MEMOCODE), 2018, : 47 - 50
  • [7] Performance of Modeling Time Series Using Nonlinear Autoregressive with eXogenous input (NARX) in the Network Traffic Forecasting
    Haviluddin
    Alfred, Rayner
    [J]. 2015 INTERNATIONAL CONFERENCE ON SCIENCE IN INFORMATION TECHNOLOGY (ICSITECH), 2015, : 164 - 168
  • [8] Seismic fragility analysis using nonlinear autoregressive neural networks with exogenous input
    Sheikh, Imran A.
    Khandel, Omid
    Soliman, Mohamed
    Haase, Jennifer S.
    Jaiswal, Priyank
    [J]. STRUCTURE AND INFRASTRUCTURE ENGINEERING, 2022, 18 (09) : 1251 - 1265
  • [9] Improving the MODIS leaf area index product for a cropland with the nonlinear autoregressive neural network with eXogenous input model
    Li, Shangzhi
    Zhang, Meng
    [J]. FRONTIERS IN EARTH SCIENCE, 2023, 10
  • [10] Predicting Sediment Concentrations Using a Nonlinear Autoregressive Exogenous Neural Network
    Alarcon, Vladimir J.
    [J]. COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2019, PT III: 19TH INTERNATIONAL CONFERENCE, SAINT PETERSBURG, RUSSIA, JULY 1-4, 2019, PROCEEDINGS, PART III, 2019, 11621 : 591 - 601
12345