By Topic

Neural network-based tracking control for robotic systems using only position feedback

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
$33 $33
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

1 Author(s)
Y. -C. Chang ; Dept. of Electr. Eng., Kung-Shan Inst. of Technol., Tainan Hsien, Taiwan

An adaptive neural network-based position feedback tracking control scheme for robotic systems involving plant uncertainties and external disturbances is proposed. The developed controller is based on a neural network system and a linear reduced-order observer. The resulting closed-loop system guarantees a transient and asymptotic performance, in the sense that the tracking error locally converges to a small region around zero in terms of L bound and H performance. The implementation of the neural network basis functions depends only on the desired reference information. Only position measurements are required for the feedback, and the developed controller is driven by the position tracking error. Consequently, the adaptive neural network-based controller developed possesses the properties of computational simplicity and easy implementation. Finally, a simulation example is provided to illustrate the tracking performance of a two-link robotic manipulator

Published in:

IEE Proceedings - Control Theory and Applications  (Volume:148 ,  Issue: 1 )