Improve consensus via decentralized predictive mechanismsH.-T. Zhang1, 2, M. Z. Q. Chen3, 4 and T. Zhou5, 6
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
received 13 February 2009; accepted in final form 30 April 2009; published May 2009
published online 5 June 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.
05.65.+b - Self-organized systems.
87.17.Jj - Cell locomotion, chemotaxis.
89.75.-k - Complex systems.
© EPLA 2009