By Topic

Control of Adept One SCARA robot using neural networks

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 $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

2 Author(s)
Meng Joo Er ; Sch. of Electr. & Electron. Eng., Nanyang Technol. Inst., Singapore ; Kang Chew Liew

This paper presents an enhanced feedback error learning control (EFELC) strategy for an n-degree-of-freedom robotic manipulator. It covers the design and simulation study of the neural network-based controller for the manipulator with a view of tracking a predetermined trajectory of motion in the joint space. An industrial robotic manipulator, the Adept One Robot, was used to evaluate the effectiveness of the proposed scheme. The Adept One Robot was simulated as a three-axis manipulator with the dynamics of the tool (fourth link) neglected and the mass of the load incorporated into the mass of the third link. For simplicity, only the first two joints of the manipulator were considered in the simulation study. The overall performance of the control system under different conditions, namely, trajectory tracking, variations in trajectory and different initial weight values were studied and comparison made with the existing feedback error learning control strategy. The enhanced version was shown to outperform the existing method

Published in:

IEEE Transactions on Industrial Electronics  (Volume:44 ,  Issue: 6 )