• Mar 24, 2016 News!Vol.3, No.3 has been indexed by EI(Inspec)!   [Click]
  • Aug 14, 2017 News!IJMMM Vol.6, No.1 has been published with online version. 15 peer reviewed articles are published in this issue.   [Click]
  • Aug 11, 2017 News!IJMMM Vol.5, No.4 has been published with online version. 15 peer reviewed articles are published in this issue.   [Click]
General Information
    • ISSN: 1793-8198
    • Frequency: Quarterly
    • DOI: 10.18178/IJMMM
    • Editor-in-Chief: Prof. K. M. Gupta, Prof. Ian McAndrew
    • Executive Editor: Ms. Cherry L. Chen
    • Abstracting/Indexing: EI (INSPEC, IET), Chemical Abstracts Services (CAS), Engineering & Technology Digital Library,  ProQuest, Crossref, Ulrich's Periodicals Directory, DOAJ, and Electronic Journals Library .
    • E-mail ijmmm@ejournal.net
Editor-in-chief
Prof. Ian McAndrew
Embry Riddle Aeronautical University, UK.
It is my honor to be the editor-in-chief of IJMMM. I will do my best to help develop this journal better.

IJMMM 2015 Vol.3(2): 80-85 ISSN: 1793-8198
DOI: 10.7763/IJMMM.2015.V3.171

A Control Scheme for Industrial Robots Using Artificial Neural Networks

M. Dinary, Abou-Hashema M. El-Sayed, Abdel Badie Sharkawy, and G. Abouelmagd
Abstract—This paper develops a new model-free control scheme based on artificial neural networks (ANN) for trajectory tracking applied on industrial manipulators. This scheme is developed to control arm robot manipulator without calculate the model parameters or dynamics, and use the online identification instead. The scheme consists of three parts. These parts are inverse identification part, ANN controller and linear controller. Inverse dynamics of the manipulator is identified by recurrent ANN that gives the identified torque. The ANN controller works on controlling the arm robot depends on the identifying torque. The linear controller designed for trajectory tracking error regulation. The identification and control ANN work together to improve the response of the linear controller. A simulated two-link arm robot is used to apply the control scheme on it. The scheme verified by mass variation. A comparison between the response of the manipulator with linear controller only and with the fully scheme has been carried out. The results show that adding the identification and control ANN improve the results of the linear controller.

Index Terms—Industrial robots, ANN, online identification, neural control, parametric and payload uncertainty.

M. Dinary is with the Mechatronics and Industrial Robotics Program, Faculty of Engineering, Minia University, El-Minia, Egypt (e-mail: MohamedDinary@mu.edu.eg).
Abou-Hashema M. El-Sayed is with the Electrical Engineering Department, Faculty of Engineering, Minia University, El-Minia, Egypt (e-mail: abouhashema@mu.edu.eg).
Abdel Badie Sharkawy is with the Mechanical Engineering Department, Faculty of Engineering, Assiut University, Assiut, Egypt (e-mail: Ab.shark@aum.edu.eg).
G. Abouelmagd is with the Production Engineering and Design Department, Faculty of Engineering, Minia University, El-Minia, Egypt (e-mail: G_magd@yahoo.com).

[PDF]

Cite: M. Dinary, Abou-Hashema M. El-Sayed, Abdel Badie Sharkawy, and G. Abouelmagd, "A Control Scheme for Industrial Robots Using Artificial Neural Networks," International Journal of Materials, Mechanics and Manufacturing vol. 3, no. 2, pp. 80-85, 2015.

Copyright © 2008-2015. International Journal of Materials, Mechanics and Manufacturing. All rights reserved.
E-mail: ijmmm@ejournal.net