Joint parameters identification for redundant manipulators based on fuzzy theory and genetic algorithm

Yangmin Li, Xiaoping Liu, Zhaoyang Peng, Yugang Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

The joint parameters of redundant manipulators are prerequisite data for effetive dynamics control. An identification method via fuzzy theory and Genetic Algorithm has been presented to study modular redundant robots. The Genetic Algorithm is used in the fuzzy optimization expecting to obtain global optimal solutions. Experimental modal analysis and Finite Element Method have been exploited in dynamics modeling. The joint parameters of a 9-DOF modular redundant robot have been identified.
Original languageEnglish
Pages (from-to)560-565
Number of pages6
JournalCanadian Conference on Electrical and Computer Engineering
Volume1
DOIs
Publication statusPublished - 1 Jan 2002
Externally publishedYes

Keywords

  • Modular robot
  • Parameter identification

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering

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