Improved Weighted Average Prediction for Multi-Agent Networks Academic Article

abstract

• In sense of communication delays, an improved robust consensus algorithm for multi-agent networks and its the convergence rate have been investigated in this paper. Precisely, an improved weighted average prediction has been introduced to reformulate the network model into a neutral network fashion. By virtue of analyzing the Hopf bifurcation, an upper bound of the communication delay is derived for the multi-agent network, which could guarantee the network to achieve weighted average consensus. In addition, the main results show that not only can the proposed method promote the robustness but also improve its convergence rate. Finally, two numerical simulations are provided, which demonstrates the effectiveness of the method. 2013 Springer Science+Business Media New York.

authors

• Huang, Tingwen

published proceedings

• CIRCUITS SYSTEMS AND SIGNAL PROCESSING

author list (cited authors)

• Wang, H., Liao, X., Huang, T., & Li, C.

citation count

• 5

complete list of authors

• Wang, Huiwei||Liao, Xiaofeng||Huang, Tingwen||Li, Chaojie

publication date

• June 2014

publisher

• Springer Nature  Publisher

published in

• Circuits, Systems and Signal Processing  Journal

keywords

• Convergence Speed
• Multi-agent Networks
• Robustness
• Weighted Average Consensus

Digital Object Identifier (DOI)

• 10.1007/s00034-013-9717-x

start page

• 1721

end page

• 1736

volume

• 33

issue

• 6

URL

• http://dx.doi.org/10.1007/s00034-013-9717-x