TY - JOUR
T1 - Closed-Loop Control of Complex Networks
T2 - A Trade-Off between Time and Energy
AU - Sun, Yong Zheng
AU - Leng, Si Yang
AU - Lai, Ying-Cheng
AU - Grebogi, Celso
AU - Lin, Wei
N1 - Funding Information:
W. L. is supported by the National Science Foundation of China (NSFC) (Grants No. 11322111 and No. 61773125). Y.-Z. S. is supported by the NSFC (Grant No. 61403393). Y.-C. L. acknowledges support from the Vannevar Bush Faculty Fellowship program sponsored by the Basic Research Office of the Assistant Secretary of Defense for Research and Engineering and funded by the Office of Naval Research through Grant No. N00014-16-1-2828.
Publisher Copyright:
© 2017 American Physical Society.
PY - 2017/11/7
Y1 - 2017/11/7
N2 - Controlling complex nonlinear networks is largely an unsolved problem at the present. Existing works focus either on open-loop control strategies and their energy consumptions or on closed-loop control schemes with an infinite-time duration. We articulate a finite-time, closed-loop controller with an eye toward the physical and mathematical underpinnings of the trade-off between the control time and energy as well as their dependence on the network parameters and structure. The closed-loop controller is tested on a large number of real systems including stem cell differentiation, food webs, random ecosystems, and spiking neuronal networks. Our results represent a step forward in developing a rigorous and general framework to control nonlinear dynamical networks with a complex topology.
AB - Controlling complex nonlinear networks is largely an unsolved problem at the present. Existing works focus either on open-loop control strategies and their energy consumptions or on closed-loop control schemes with an infinite-time duration. We articulate a finite-time, closed-loop controller with an eye toward the physical and mathematical underpinnings of the trade-off between the control time and energy as well as their dependence on the network parameters and structure. The closed-loop controller is tested on a large number of real systems including stem cell differentiation, food webs, random ecosystems, and spiking neuronal networks. Our results represent a step forward in developing a rigorous and general framework to control nonlinear dynamical networks with a complex topology.
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U2 - 10.1103/PhysRevLett.119.198301
DO - 10.1103/PhysRevLett.119.198301
M3 - Article
C2 - 29219507
AN - SCOPUS:85033594150
SN - 0031-9007
VL - 119
JO - Physical Review Letters
JF - Physical Review Letters
IS - 19
M1 - 198301
ER -