American Journal of Artificial Intelligence
Volume 1, Issue 1, December 2017, Pages: 56-61
Received: Apr. 26, 2017;
Accepted: Jul. 25, 2017;
Published: Nov. 28, 2017
Views 2058 Downloads 211
Ayman A. Aly, Mechanical Engineering Dept., College of Engineering, Taif University, Taif, Saudi Arabia; Mechanical Engineering Dept., Faculty of Engineering, Assiut University, Assiut, Egypt
Aloqla A., Mechanical Engineering Dept., College of Engineering, Taif University, Taif, Saudi Arabia
Robot manipulators have become increasingly important in the field of automation. So modelling and control of robots in automation will be very important. This paper presents a study of robust control approach employing fuzzy logic control technique for two degree of freedom (2-DOF) manipulator robot. A learning control system is designed so that its “learning mechanism” has the ability to improve the performance of the closed-loop system by generating command inputs to the plant and utilizing feedback information from the plant. It is well known that robotic manipulators are highly nonlinear coupling dynamic systems. A fuzzy logic rule base is designed, using the knowledge obtained from the operator. Simulation is performed to demonstrate the effectiveness of control strategy. Furthermore, the parameters of the controllers were optimized using MATLAB and simulations' result reveals that control scheme is working satisfactorily.
Ayman A. Aly,
Robust Fuzzy Control for 2-DOF Manipulator System, American Journal of Artificial Intelligence.
Vol. 1, No. 1,
2017, pp. 56-61.
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABDALLAH, C., D Awso N, D. M., Do RATO, P., and JAMrsmm, M., 1991, Survey of the robust control of robots. IEEE Control Systems Magazine, 11, (2), 24-30.
Khalil A. Khalil, Ayman A. Aly, and A. Abo-Ismail, “Adaptive Based Block Fuzzy Controller of Flexible Robot Arm”, IASTED International Conference on Intelligent Systems and Control, Orlando, USA, November 16 - 18, 2008.
Ayman A. Aly, Farhan A. Salem, "Design of Intelligent Position Control for Single Axis Robot Arm", International Journal of Scientific & Engineering Research, IJSER, Volume 5, Issue 6, pp 790-795, 2014.
BARNETT, S., 1984, Matrices in Control Theory (Malabar, Fla.: Robert E. Krieger).
CORLESS, M., 1989, Tracking controllers for uncertain systems: application to a mantic R3 robot. Journal of Dynamic Systems, Measurement and Control, 111, 609-618.
CORLESS, M. J., and LEITMANN, G., 1981, Continuous state feedback guaranteeing uniform ultimate boundedness for uncertain dynamic systems. IEEE Transactions on Automatic Control, 26, 1139-1143.
Ayman A. Aly and Aly S. Abo El-Lail, “Self Tuning PI Fuzzy Position Control of A Hydraulic Manipulator” Journal of Engineering Science, JES, Vol. 33, No. 1, pp 199-209, January 2005.
Ayman A. Aly, “Motion Control Of A Mechatronics System Using Fuzzy Model Reference Learning Technique”, Multidisciplinary International Peer Reviewed Journal, SAUSSUREA, Vol. 5 (6): PP. 96-102, 2015
J. J. E. Slotine and W. Li, Applied nonlinear control vol. 461: Prentice hall Englewood Cliffs, NJ, 1991.
Ayman A. Aly, “Motion Control of A Mechatronics System Using Fuzzy Model Reference Learning Technique”, Multidisciplinary International Peer Reviewed Journal, SAUSSUREA, Vol. 5 (6): PP. 96-102, 2015.