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A new neural network control technique for robot manipulators*

Published online by Cambridge University Press:  09 March 2009

Seul Jung  and
T. C. Hsia
Seul Jung
Affiliation:
Robotics Research Laboratory, Department of Electrical and Computer Engineering, University of California, Davis, CA 95616 (USA)
T. C. Hsia
Affiliation:
Robotics Research Laboratory, Department of Electrical and Computer Engineering, University of California, Davis, CA 95616 (USA)
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Summary

A new neural network (NN) control technique for robot manipulators is introduced in this paper. The fundamental robot control technique is the model-based computed-torque control which is subjected to performance degradation due. to model uncertainty. NN controllers have been traditionally used to generate a compensating joint torque to account for the effects of the uncertainties. The proposed NN control approach is conceptually different in that it is aimed at prefiltering the desired joint trajectories before they are used to command the computed-torque-controlled robot system (the plant) to counteract performance degradation due to plant uncertainties. In this framework, the NN controller serves as the inverse model of the plant, which can be trained on-line using joint tracking error. Several variations of this basic technique is introduced; Back-propagation training algorithms for the NN controller have been developed. Simulation results have demonstrated the excellent tracking performance of the proposed control technique.

Keywords

Neural networkControlRobotsComputed torque.
Type
Articles
Information
Robotica , Volume 13 , Issue 5 , September 1995 , pp. 477 - 484
Copyright
Copyright © Cambridge University Press 1995

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References

1.Psaltis, D.. Sideris, A. and Yamamura, A., “A Multriayered Neural Network Controllers” IEEE Control System Magazine 17–21 (1986).CrossRefGoogle Scholar
2.Chen, V.C. and Pao, Y.H., “Learning Control with Neural Network” IEEE Control System Magazine 1448–1453 (1989).Google Scholar
3.Chen, F.C., “Back Propagation Neural Networks for Nonlinear Self-tuning Adaptive Control” IEEE Control System Magazine 10 (3), 44–48 (1990).CrossRefGoogle Scholar
4.Kuschewski, J.G., Hui, S. and Zak, S.H., “Application of Feedforward Neural Networks to Dynamical System Identification and Control” IEEE Trans, on Control Systems Technology 1, 37–49 (1993).CrossRefGoogle Scholar
5.Narendra, K. and Parthasarathy, K., “Identification and Control of Dynamical Systems Using Neural Networks” IEEE Trans, on Neural Networks 1(1), 4–27 (1990).CrossRefGoogle ScholarPubMed
6.Yabuta, T. and Yamada, T., “Possibility of Neural Networks Controller for Robot Manipulator” Proc. of Robotics and Automation (1990) pp. 1686–1691.Google Scholar
7.Ku, C.C. and Lee, K.Y., “Diagonal Recurrent Neural Networks for Nonlinear System Control” American Control Conference (1992) pp. 545–549.Google Scholar
8.Ishiguro, A., Furuhashii, T., Okuma, S. and Uchikawa, Y., “A Neural Network Compensator for Uncertainties or Robot Manipulator” IEEE Trans, on Industrial Electronics 39, 61–66 (December, 1992).Google Scholar
9.Miyamoto, H., Kawato, M., Setoyama, T. and Suzuki, R., “Feedback Error Learning Neural Network for Trajectory Control of a Robotic Manipulator” IEEE Trans, on Neural Networks, 1, 251–265 (1988).CrossRefGoogle Scholar
10.Ozaky, T., Suzuki, T., Furuhashi, T. and Okuma, S. and Uchikawa, Y. “Trajectory Control of Robotic Manipulators Using Neural Networks” IEEE Trans, on Industrial Electronics 38,195–202 (1991).CrossRefGoogle Scholar
11.Gehlot, N.S. and Alsina, P.J., “A Comparison Control Strategies of Robotic Manipulators Using Neural Networks” International Conference on IECON (1992) pp. 688–693.Google Scholar
12.Jung, S. and Hisa, T.C., “On-Line Neural Network Control of Robot Manipulators” International Conference on Neural Information Processing (October, 1994) pp. 1636–1668Google Scholar