Nonlinearly-activated noise-tolerant zeroing neural network for distributed motion planning of multiple robot arms

Long Jin, Shuai Li, Xin Luo, Ming Sheng Shang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

This paper investigates the distributed motion planning of multiple robot arms with limited communications in the presence of noises. To do this, a nonlinearly-activated noise-tolerant zeroing neural network (NANTZNN) is designed and presented for the first time for solving the presented distributed scheme online. Theoretical analyses and simulation results show the effectiveness and accuracy of the presented distributed scheme with the aid of NANTZNN model.

Original languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4165-4170
Number of pages6
ISBN (Electronic)9781509061815
DOIs
Publication statusPublished - 30 Jun 2017
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: 14 May 201719 May 2017

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2017-May

Conference

Conference2017 International Joint Conference on Neural Networks, IJCNN 2017
Country/TerritoryUnited States
CityAnchorage
Period14/05/1719/05/17

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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