T-S fuzzy-neural control for robot manipulators

Wei Yen Wang*, Yi Hsing Chien, Yih Guang Leu, Zheng Hao Lee, Tsu Tian Lee

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

This paper proposes a novel method of on-line modeling and control through the Takagi-Sugeno (T-S) fuzzy-neural model for a class of general n-link robot manipulators. Compared with the previous method, the main contribution of this paper is an investigation of the more general robot systems using on-line adaptive T-S fuzzy-neural controller. Specifically, the general robot systems are exactly formed a linearized system via the mean value theorem, and then the T-S fuzzy-neural model can approximate the linearized system. Also, we propose an online identification algorithm and put significant emphasis on robust tracking controller design using an adaptive scheme for the robot systems. Finally, an example including two cases is provided to demonstrate feasibility and robustness of the proposed method.

Original languageEnglish
Title of host publicationIEEE International Conference on Advanced Robotics and its Social Impacts, ARSO 2008
DOIs
Publication statusPublished - 2008
EventIEEE International Conference on Advanced Robotics and its Social Impacts, ARSO 2008 - Taipei, Taiwan
Duration: 2008 Aug 232008 Aug 25

Publication series

NameProceedings of IEEE Workshop on Advanced Robotics and its Social Impacts, ARSO
ISSN (Print)2162-7568
ISSN (Electronic)2162-7576

Other

OtherIEEE International Conference on Advanced Robotics and its Social Impacts, ARSO 2008
Country/TerritoryTaiwan
CityTaipei
Period2008/08/232008/08/25

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition

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