Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-19T02:18:18.669Z Has data issue: false hasContentIssue false
  • English
  • Français

On-line learning control of manipulators based onartificial neural network models

Published online by Cambridge University Press:  01 May 1997

M. Kemal Ciliz  and
Can Işik
M. Kemal Ciliz
Affiliation:
Electrical Engineering Department, Bogaziçi University, Bebek, Istanbul 80815, Turkey
Can Işik
Affiliation:
Electrical and Computer Engineering Department, Syracuse University, Syracuse, NY 13244-1240, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper addresses the tracking control problem of robotic manipulators with unknown and changing dynamics. In this study, nonlinear dynamics of the robotic manipulator is assumed to be unknown and a control scheme is developed to adaptively estimate the unknown manipulator dynamics utilizing generic artificial neural network models to approximate the underlying dynamics. Based on the error dynamics of the controller, a parameter update equation is derived for the adaptive ANN models and local stability properties of the controller are discussed. The proposed scheme is simulated and successfully tested for trajectory following tasks. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics.

Keywords

Neural networkOn-line learningTracking controlManipulators
Type
Research Article
Information
Robotica , Volume 15 , Issue 3 , May 1997 , pp. 293 - 304
Copyright
© 1997 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

This work was in part sponsored by Westinghouse Education Foundation under contract No: 3597262.