The convergence consensus of multi-Agent systems controlled via doubly stochastic quadratic operators

Rawad Abdulghafor, Sherzod Turaev, Akram Zeki, Farruh Shahidi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

This paper proposed doubly stochastic quadratic operators (DSQOs) for a consensus problem in multi-Agent systems. The proposed scheme uses new nonlinear class model of family of quadratic stochastic operators (QSOs) for convergence consensus. The nonlinear model of QSOs plays an important role for reaching consensus. The nonlinear protocols for DSQOs are based on majorization theory. The paper investigates how the multi-Agent systems converge to the optimal values (center) by using DSQOs. The proposed nonlinear model of DSQOs will be compared with the linear model of DeGroot and the nonlinear model of QSOs. Furthermore, we will show that the convergence of DSQOs is superior than DeGroot linear model and low-complex than QSOs nonlinear model.

Original languageEnglish
Title of host publication2015 International Symposium on Agents, Multi-Agent Systems and Robotics, ISAMSR 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages59-64
Number of pages6
ISBN (Electronic)9781509002788
DOIs
Publication statusPublished - Jan 12 2016
Externally publishedYes
Event1st International Symposium on Agents, Multi-Agent Systems and Robotics, ISAMSR 2015 - Putrajaya, Malaysia
Duration: Aug 18 2015Aug 19 2015

Publication series

Name2015 International Symposium on Agents, Multi-Agent Systems and Robotics, ISAMSR 2015

Conference

Conference1st International Symposium on Agents, Multi-Agent Systems and Robotics, ISAMSR 2015
Country/TerritoryMalaysia
CityPutrajaya
Period8/18/158/19/15

Keywords

  • consensus problem
  • convergence
  • doubly stochastic quadratic operators
  • multi-Agent systems

ASJC Scopus subject areas

  • Decision Sciences (miscellaneous)
  • Computer Science Applications
  • Artificial Intelligence

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