Bi-criteria torque optimization of redundant manipulators based on a simplified dual neural network

Shubao Liu, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Citations (Scopus)

Abstract

The bi-criteria joint torque optimization of kinematically redundant manipulators balances between the energy consumption and the torque distribution among the joints. In this paper, a simplified dual neural network is proposed to solve this problem. Joint torque limits are incorporated simultaneously into the proposed optimization scheme. The simplified dual network has less numbers of neurons compared with other recurrent neural networks and is proved to be globally convergent to optimal solutions. The control scheme based on the recurrent neural network is simulated with the PUMA 560 robot manipulator to demonstrate effectiveness.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks, IJCNN 2005
Pages2796-2801
Number of pages6
DOIs
Publication statusPublished - 2005
Externally publishedYes
EventInternational Joint Conference on Neural Networks, IJCNN 2005 - Montreal, QC, Canada
Duration: 31 Jul 20054 Aug 2005

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume5

Conference

ConferenceInternational Joint Conference on Neural Networks, IJCNN 2005
Country/TerritoryCanada
CityMontreal, QC
Period31/07/054/08/05

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