Back
pdf
Journal article

Improve consensus via decentralized predictive mechanisms

  • Zhang, Hai-Tao Key Laboratory of Image Processing and Intelligent Control, Department of Control Science and Engineering, Huazhong University of Science and Technology - Wuhan, PRC - State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology - Wuhan, PRC
  • Chen, Michael ZhiQiang Department of Automation, Nanjing University of Science and Technology - Nanjing, PRC - Department of Engineering, University of Leicester, UK
  • Zhou, Tao Department of Modern Physics, University of Science and Technology of China - Hefei, PRC - Department of Physics, University of Fribourg, Switzerland
    05.06.2009
Published in:
  • EPL Europhysics Letters. - 2009, vol. 86, p. 40011
English 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.
Faculty
Faculté des sciences et de médecine
Department
Département de Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/301307
Statistics
Document views: 49 File downloads:
  • zhou_icd.pdf: 134