A dual neural network for bi-criteria torque optimization of redundant robot manipulators

Shubao Liu, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

7 Citations (Scopus)

Abstract

A dual neural network is presented for the bi-criteria joint torque optimization of kinematically redundant manipulators, which balances between the total energy consumption and the torque distribution among the joints. Joint torque limits are also incorporated simultaneously into the proposed optimization scheme. The dual neural network has a simple structure with only one layer of neurons and is proven to be globally exponentially convergent to the optimal solution. The effectiveness of dual neural network for this problem is demonstrated by simulation with the PUMA560 manipulator.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsNikhil R. Pal, Srimanta Pal, Nikola Kasabov, Rajani K. Mudi, Swapan K. Parui
PublisherSpringer Verlag
Pages1142-1147
Number of pages6
ISBN (Print)3540239316, 9783540239314
DOIs
Publication statusPublished - 2004
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3316
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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