Upper bound of the minimum energy cost for controlling complex networks

Gaopeng Duan, Aming Li, Tao Meng, Guofeng Zhang, Long Wang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


For controlling complex systems ultimately, recent years have witnessed a surge of interest in the controllability of complex networks. Controllability, a system's basic attribute, represents whether we could control the system from any initial to any final states with appropriate external inputs within a finite time. However, in order to implement control in realistic systems, it is far from enough by solely detecting the so-called controllability. In other words, the minimum energy cost, with which to actuate the evolution of system states, must be systematically investigated to accomplish the control of various complex systems. Here we show the results on the scaling behavior of control energy for controlling complex networks. Specifically, we focus on the upper bound of the minimum control energy over all possible control directions within a certain control distance between the initial and final states. Numerical validations on all of our theoretical results are also provided. Our results pave the way to implement realistic control over various complex networks with the minimum energy cost.

Original languageEnglish
Title of host publicationProceedings of the 38th Chinese Control Conference, CCC 2019
EditorsMinyue Fu, Jian Sun
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9789881563972
Publication statusPublished - 27 Jul 2019
Event38th Chinese Control Conference, CCC 2019 - Guangzhou, China
Duration: 27 Jul 201930 Jul 2019

Publication series

NameChinese Control Conference, CCC
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927


Conference38th Chinese Control Conference, CCC 2019


  • Complex Networks
  • Controllability
  • Energy Cost
  • Scaling Behavior
  • Upper Bound

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Applied Mathematics
  • Modelling and Simulation


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