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

Yangmin Li; Xiaoping Liu; Zhaoyang Peng; Yugang Liu

doi:10.1109/CCECE.2002.1015288

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 publication › Journal article › Academic research › peer-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 language	English
Pages (from-to)	560-565
Number of pages	6
Journal	Canadian Conference on Electrical and Computer Engineering
Volume	1
DOIs	https://doi.org/10.1109/CCECE.2002.1015288
Publication status	Published - 1 Jan 2002
Externally published	Yes

Keywords

Modular robot
Parameter identification

ASJC Scopus subject areas

Hardware and Architecture
Electrical and Electronic Engineering

More information

10.1109/CCECE.2002.1015288

Cite this

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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.",

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AU - Peng, Zhaoyang

AU - Liu, Yugang

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AB - 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.

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KW - Parameter identification

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DO - 10.1109/CCECE.2002.1015288

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JO - Canadian Conference on Electrical and Computer Engineering

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ER -

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

Abstract

Keywords

ASJC Scopus subject areas

More information

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