Enabling controlling complex networks with local topological information
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...
| Main Authors: | , , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
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2018
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| Online Access: | https://hdl.handle.net/10356/87366 http://hdl.handle.net/10220/45397 |
| _version_ | 1826113208596824064 |
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| author | Li, Guoqi Deng, Lei Xiao, Gaoxi Tang, Pei Wen, Changyun Hu, Wuhua Pei, Jing Shi, Luping Stanley, H. Eugene |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Li, Guoqi Deng, Lei Xiao, Gaoxi Tang, Pei Wen, Changyun Hu, Wuhua Pei, Jing Shi, Luping Stanley, H. Eugene |
| author_sort | Li, Guoqi |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T03:19:27Z |
| format | Journal Article |
| id | ntu-10356/87366 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:19:27Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/873662020-03-07T13:57:28Z Enabling controlling complex networks with local topological information Li, Guoqi Deng, Lei Xiao, Gaoxi Tang, Pei Wen, Changyun Hu, Wuhua Pei, Jing Shi, Luping Stanley, H. Eugene School of Electrical and Electronic Engineering Structural Controllability Optimal Control 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. NRF (Natl Research Foundation, S’pore) MOE (Min. of Education, S’pore) Published version 2018-07-31T01:16:16Z 2019-12-06T16:40:23Z 2018-07-31T01:16:16Z 2019-12-06T16:40:23Z 2018 Journal Article 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-. 2045-2322 https://hdl.handle.net/10356/87366 http://hdl.handle.net/10220/45397 10.1038/s41598-018-22655-5 en Scientific Reports © 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/ 10 p. application/pdf |
| spellingShingle | Structural Controllability Optimal Control Li, Guoqi Deng, Lei Xiao, Gaoxi Tang, Pei Wen, Changyun Hu, Wuhua Pei, Jing Shi, Luping Stanley, H. Eugene Enabling controlling complex networks with local topological information |
| title | Enabling controlling complex networks with local topological information |
| title_full | Enabling controlling complex networks with local topological information |
| title_fullStr | Enabling controlling complex networks with local topological information |
| title_full_unstemmed | Enabling controlling complex networks with local topological information |
| title_short | Enabling controlling complex networks with local topological information |
| title_sort | enabling controlling complex networks with local topological information |
| topic | Structural Controllability Optimal Control |
| url | https://hdl.handle.net/10356/87366 http://hdl.handle.net/10220/45397 |
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