Control of Adept One SCARA robot using neural networks

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 jo...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 1997-12, Vol.44 (6), p.762-768
Main Authors: Er, M J, Liew, K C
Format: Article
Language:English
Subjects:
Control systems
Error correction
Manipulator dynamics
Motion control
Neural networks
Neurofeedback
Orbital robotics
Service robots
Tracking
Trajectory
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0278-0046
1557-9948
DOI:10.1109/41.649936