Robust Adaptive Neural-Fuzzy Network Tracking Control for Robot Manipulator
Abstract
This paper presents a robust adaptive neural-fuzzy network control (RANFNC) system for an n-link robot manipulator to achieve the highprecision position tracking. Initially, the model dynamic of an n-link robot manipulator is introduced. However, it is difficult to design a conformable model-based control scheme, for instance, external disturbances, friction forces and parameter variations. In order to deal with this problem, the RANFNC system is investigated to the joint position control of an n-link robot manipulator. In this control scheme, a four-layer neural-fuzzy-network (NFN) is used for the main role, and the adaptive tuning laws of network parameters are derived in the sense of a projection algorithm and the Lyapunov stability theorem to ensure network convergence as well as stable control performance. The merits of this model-free control scheme are that not only the stable position tracking performance can be guaranteed but also unknown system information and auxiliary control design are required in the control process. The simulation results are provided to verify the effectiveness of the proposed RANFNC methodology.References
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[3] Shuzhi S. Ge, Adaptive neural network control of robot manipulator in task space, IEEE rans. Ind. Electron, 44(6):746-752, 1997
http://dx.doi.org/10.1109/41.649934
[4] C.T. Lin and C.S. George Lee, Neural Fuzzy Systems, Englewood Cliffs, Prentice-Hall, 1996
[5] Y.Q. Zhang and A. Kandel, Compensatory neural-fuzzy systems with fast learning algorithms, EEE Trans. Neural Newt, 9(1):83-105, 1998
http://dx.doi.org/10.1109/72.655032
[6] Vesselenyi T., Dzitac S., Dzitac I., Manolescu M.-J., Fuzzy and Neural Controllers for a neumatic Actuator, NT J COMPUT COMMUN, ISSN 1841-9836. 2(4): 375-387, 2007
[7] Alavandar S., Nigam M.J., Neuro-Fuzzy based Approach for Inverse Kinematics Solution of ndustrial Robot Manipulators, INT J COMPUT COMMUN, ISSN 1841-9836, 3(3):224-234, 008
[8] AlavandarS., Nigam M.J., Inverse Kinematics Solution of 3DOF Planar Robot using ANFIS, T J COMPUT COMMUN, ISSN 1841-9836, 3(S):150-155, 2008
[9] L.X. Wang, A course in Fuzzy Systems and Control, Englewood Cliffs, NJ:Prentice Hall, 1997
[10] O. Omidvar and D.L. Elliott, Neural Systems for Control, Englewood Cliffs, NJ: Prentice- all, 1997
[11] B.S. Chen, H.J. Uang, and C.S. Tseng, Robust tracking enhancement of robot systems ncluding motor dynamics: A fuzzy-based dynamic game approach, IEEE Trans. Fuzzy syst, 1(4):538-553, 1998
http://dx.doi.org/10.1109/91.728449
[12] R.J. Schilling, Fundamentals of Robotics, Analysis and control. Hoboken, NJ: Prentice-Hall, 998
[13] J.J.E. Slotime and W. Li, Applied Nonlinear Control, Hoboken, NJ: Prentice-Hall, 1991
[14] H. K. Khalil, Nonlinear Systems. Englewood Cliffs, Englewood Cliffs, NJ: Prentice-Hall, 996
[15] K. Liu and F.L. Lewis, Robust control techniques for general dynamic system, J. intell. obotic syst, 6:33-49, 1992
[16] F.L. Lewis, C.T. Abdallah, and D.M Dawson, Control of Robot Manipulators, New York: acmillan, 1993
Published
2014-09-20
How to Cite
NGO, ThanhQuyen et al.
Robust Adaptive Neural-Fuzzy Network Tracking Control for Robot Manipulator.
INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 7, n. 2, p. 341-352, sep. 2014.
ISSN 1841-9844.
Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/1414>. Date accessed: 05 july 2020.
doi: https://doi.org/10.15837/ijccc.2012.2.1414.
Section
Articles
Keywords
Adaptive control, Neural-fuzzy network, robot manipulator
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