Hostname: page-component-cb9f654ff-c75p9 Total loading time: 0 Render date: 2025-08-19T22:50:36.872Z Has data issue: false hasContentIssue false
  • English
  • Français

Trajectory control of robotic manipulators by using a feedback-error-learning neural network

Published online by Cambridge University Press:  09 March 2009

Zaryab Hamavand  and
Howard M. Schwartz
Zaryab Hamavand
Affiliation:
Department of Systems and Computer Engineering, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario, (Canada) KIS5B6
Howard M. Schwartz
Affiliation:
Department of Systems and Computer Engineering, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario, (Canada) KIS5B6
Get access Rights & Permissions [Opens in a new window]

Summary

This paper presents a neural network based control strategy for the trajectory control of robot manipulators. The neural network learns the inverse dynamics of a robot manipulator without any a priori knowledge of the manipulator inertial parameters nor any a priori knowledge of the equation of dynamics. A two step feedback-error-learning process is proposed. Strategies for selection of the training trajectories and difficulties with on-line training are discussed.

Keywords

Neural networksTrajectory trackingRobot control

Information

Type
Articles
Information
Robotica , Volume 13 , Issue 5 , September 1995 , pp. 449 - 459
Copyright
Copyright © Cambridge University Press 1995

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.)

Article purchase

Temporarily unavailable

References

1.Khemaissia, S., and Morris, A.S., ‘Neuro-adaptive control of robotic manipulators’ Robotica 11, Part 5, 465–473 (1993).CrossRefGoogle Scholar
2.Miyamoto, H., Kawato, M., Setoyama, T. and Suzuki, R., ‘Feedback-error-learning neural networks for trajectory control of a robotic manipulator’ Neural Networks 1, 251–265, (1988).CrossRefGoogle Scholar
3.Newton, R.T. and Xu, Y., ‘Neural Network Control of a Space Manipulator’ IEEE Control Systems 13, No. 6, 14–22 (1993).Google Scholar
4.Miller, W.T., Glanz, F.H. and Kraft, L.G., ‘CMAC: An associative neural network alternative to backpropagation’ Proc. IEEE 78, No. 10, 1561–1567 (1990).Google Scholar
5.Ozaki, T., Suzuki, T., Furuhashi, T., Okuma, S. and Uchikawa, Y., ‘Trajectory control of Robotic Manipulators Using Neural Networks’ IEEE Trans, on Industrial Electronics 38, No. 3, 195–202 (06, 1991).CrossRefGoogle Scholar