Robust Adaptive Neural-Fuzzy Network Tracking Control for Robot Manipulator

  • ThanhQuyen Ngo Faculty of Electrical Engineering HCM City University of Industry, HCM City, Vietnam
  • YaoNan Wang College of Electrical and Information Engineering Hunan University, Changsha, Hunan Province 410082, P.R.China
  • T. Long Mai College of Electrical and Information Engineering Hunan University, Changsha, Hunan Province 410082, P.R.China
  • M. Hung Nguyen
  • Jun Chen

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.

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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.

Keywords

Adaptive control, Neural-fuzzy network, robot manipulator