Issue |
EPL
Volume 86, Number 4, May 2009
|
|
---|---|---|
Article Number | 40011 | |
Number of page(s) | 6 | |
Section | General | |
DOI | https://doi.org/10.1209/0295-5075/86/40011 | |
Published online | 05 June 2009 |
Improve consensus via decentralized predictive mechanisms
1
Key Laboratory of Image Processing and Intelligent Control, Department of Control Science and Engineering, Huazhong University of Science and Technology - Wuhan 430074, PRC
2
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology - Wuhan, 430074, PRC
3
Department of Automation, Nanjing University of Science and Technology - Nanjing 210094, PRC
4
Department of Engineering, University of Leicester - Leicester LE1 7RH, UK, EU
5
Department of Modern Physics, University of Science and Technology of China - Hefei 230026, PRC
6
Department of Physics, University of Fribourg, Chemin du Muse - CH-1700 Fribourg, Switzerland
Corresponding authors: michael.chen@cantab.net mzqchen@gmail.com
Received:
13
February
2009
Accepted:
30
April
2009
For biogroups and groups of self-driven agents, making decisions often depends on interactions among group members. In this paper, we seek to understand the fundamental predictive mechanisms used by group members in order to perform such coordinated behaviors. In particular, we show that the future dynamics of each node in the network can be predicted solely using local information provided by its neighbors. Using this predicted future dynamics information, we propose a decentralized predictive consensus protocol, which yields drastic improvements in terms of both consensus speed and internal communication cost. In natural science, this study provides an evidence for the idea that some decentralized predictive mechanisms may exist in widely-spread biological swarms/flocks. From the industrial point of view, incorporation of a decentralized predictive mechanism allows for not only a significant increase in the speed of convergence towards consensus but also a reduction in the communication energy required to achieve a predefined consensus performance.
PACS: 05.65.+b – Self-organized systems / 87.17.Jj – Cell locomotion, chemotaxis / 89.75.-k – Complex systems
© EPLA, 2009
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.