By Topic

Real time control of robot manipulator using a neural network based learning controller

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

1 Author(s)
Chan, S.P. ; Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore

A neurocontroller is presented for the tracking control of a SCARA robot. The structure of the controller consists of an inverse dynamics model of which the parameters are to be learnt in real time and a feedback servo to guarantee stability. By exploiting the a priori knowledge about the dynamics of the robot, a single layer linear network is obtained to model the inverse dynamics thereby reducing the training time. Real time learning of the synaptic weights which represent the parameters of the inverse dynamics of the robot can be completed in a few minutes. Experimental results demonstrated that the performance of the neurocontroller improved rapidly during learning. Accurate trajectory tracking is achieved within the first ten presentations of the training trajectory pattern

Published in:

Industrial Electronics, Control, and Instrumentation, 1993. Proceedings of the IECON '93., International Conference on

Date of Conference:

15-19 Nov 1993