Cart (Loading....) | Create Account
Close category search window
 

Direct adaptive fuzzy-neural-network control for robot manipulator by using only position measurements

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Rong-Jong Wai ; Dept. of Electr. Eng. & Fuel Cell Center, Yuan Ze Univ., Chungli, Taiwan ; Zhi-Wei Yang ; Chih-Yi Shih

This study focuses on the development of a direct adaptive fuzzy-neural-network control (DAFNNC) for an n-link robot manipulator to achieve high-precision position tracking. In general, it is difficult to adopt a model-based design to achieve this control objective due to the uncertainties in practical applications, such as friction forces, external disturbances and parameter variations. In order to cope with this problem, a DAFNNC strategy is investigated without the requirement of prior system information. In this model-free control topology, a FNN controller is directly designed to imitate a predetermined model-based stabilizing control law, and then the stable control performance can be achieved by only using joint position information. The DAFNNC law and the adaptive tuning algorithms for FNN weights are established in the sense of Lyapunov stability analyses to ensure the stable control performance. Numerical simulations of a two-link robot manipulator actuated by DC servomotors are given to verify the effectiveness and robustness of the proposed methodology. In addition, the superiority of the proposed control scheme is indicated in comparison with proportional-differential control (PDC), fuzzy-model-based control (FMBC), T-S type fuzzy-neural-network control (T-FNNC), and robust-neural-fuzzy-network control (RNFNC) systems.

Published in:

Industrial Electronics and Applications (ICIEA), 2010 the 5th IEEE Conference on

Date of Conference:

15-17 June 2010

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.