Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87366
Title: Enabling controlling complex networks with local topological information
Authors: Li, Guoqi
Deng, Lei
Xiao, Gaoxi
Tang, Pei
Wen, Changyun
Hu, Wuhua
Pei, Jing
Shi, Luping
Stanley, H. Eugene
Keywords: Structural Controllability
Optimal Control
Issue Date: 2018
Source: Li, G., Deng, L., Xiao, G., Tang, P., Wen, C., Hu, W., et al. (2018). Enabling controlling complex networks with local topological information. Scientific Reports, 8(1), 4593-.
Series/Report no.: Scientific Reports
Abstract: Complex networks characterize the nature of internal/external interactions in real-world systems including social, economic, biological, ecological, and technological networks. Two issues keep as obstacles to fulfilling control of large-scale networks: structural controllability which describes the ability to guide a dynamical system from any initial state to any desired final state in finite time, with a suitable choice of inputs; and optimal control, which is a typical control approach to minimize the cost for driving the network to a predefined state with a given number of control inputs. For large complex networks without global information of network topology, both problems remain essentially open. Here we combine graph theory and control theory for tackling the two problems in one go, using only local network topology information. For the structural controllability problem, a distributed local-game matching method is proposed, where every node plays a simple Bayesian game with local information and local interactions with adjacent nodes, ensuring a suboptimal solution at a linear complexity. Starring from any structural controllability solution, a minimizing longest control path method can efficiently reach a good solution for the optimal control in large networks. Our results provide solutions for distributed complex network control and demonstrate a way to link the structural controllability and optimal control together.
URI: https://hdl.handle.net/10356/87366
http://hdl.handle.net/10220/45397
ISSN: 2045-2322
DOI: http://dx.doi.org/10.1038/s41598-018-22655-5
Rights: © 2018 The Author(s) (Nature Publishing Group). This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
metadata.item.grantfulltext: open
metadata.item.fulltext: With Fulltext
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