Controlling multi-link manipulators by fuzzy selection of dynamic models

T. Nanayakkara, K. Watanabe, K. Kiguchi, K. Izumi

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

3 被引用数 (Scopus)

抄録

A method for the identification of complex nonlinear dynamics of a multi-link robot manipulator using Runge-Kutta-Gill neural networks (RKGNNs) in the absence of input torque information is proposed. The RKGNNs constructed using shape adaptive radial basis functions are trained by an evolutionary algorithm. Due to the fact that the main function network is divided into sub-networks to represent the dynamic properties of the manipulator, the neural networks have greater information, processing capacity and can be tested for properties such as positive definiteness of the inertia matrix. Dynamics of a three-link manipulator are identified using only their input-output position and velocity data, and promising control results are obtained to prove the effectiveness of the proposed method in capturing highly nonlinear dynamics of a multi-link manipulator.

本文言語英語
ホスト出版物のタイトルIECON Proceedings (Industrial Electronics Conference)
出版社IEEE Computer Society
ページ638-643
ページ数6
DOI
出版ステータス出版済み - 1 1 2000
外部発表はい

出版物シリーズ

名前IECON Proceedings (Industrial Electronics Conference)
1

All Science Journal Classification (ASJC) codes

  • 制御およびシステム工学
  • 電子工学および電気工学

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