A Neural Network Compensator for Uncertainties of Robotics Manipulators

Akio Ishiguro, Takeshi Furuhashi, Shigeru Okuma, Yoshiki Uchikawa

研究成果: ジャーナルへの寄稿学術論文査読

74 被引用数 (Scopus)

抄録

Neural networks have been studied to control robotic manipulators. Most researches aimed to internalize inverse dynamic models of controlled objects. It has been difficult, however, to obtain true teaching signals of neural networks for learning unknown controlled objects. In the case of robotic manipulators, approximate models of the controlled objects can be generally derived. We believe that the neural networks perform best when they are not required to learn too much. Thus, in this paper, we propose neural networks that do not learn inverse dynamic models but compensate nonlinearities of robotic manipulators with the computed torque method. Furthermore, we show a method to obtain true teaching signals of the neural network compensators.

本文言語英語
ページ(範囲)565-570
ページ数6
ジャーナルIEEE Transactions on Industrial Electronics
39
6
DOI
出版ステータス出版済み - 1992 12月

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