Literature DB >> 25167459

Controllability transition and nonlocality in network control.

Jie Sun1, Adilson E Motter2.   

Abstract

A common goal in the control of a large network is to minimize the number of driver nodes or control inputs. Yet, the physical determination of control signals and the properties of the resulting control trajectories remain widely underexplored. Here we show that (i) numerical control fails in practice even for linear systems if the controllability Gramian is ill conditioned, which occurs frequently even when existing controllability criteria are satisfied unambiguously, (ii) the control trajectories are generally nonlocal in the phase space, and their lengths are strongly anti-correlated with the numerical success rate and number of control inputs, and (iii) numerical success rate increases abruptly from zero to nearly one as the number of control inputs is increased, a transformation we term numerical controllability transition. This reveals a trade-off between nonlocality of the control trajectory in the phase space and nonlocality of the control inputs in the network itself. The failure of numerical control cannot be overcome in general by merely increasing numerical precision--successful control requires instead increasing the number of control inputs beyond the numerical controllability transition.

Mesh:

Year:  2013        PMID: 25167459     DOI: 10.1103/PhysRevLett.110.208701

Source DB:  PubMed          Journal:  Phys Rev Lett        ISSN: 0031-9007            Impact factor:   9.161


  37 in total

1.  Networkcontrology.

Authors:  Adilson E Motter
Journal:  Chaos       Date:  2015-09       Impact factor: 3.642

2.  Realistic control of network dynamics.

Authors:  Sean P Cornelius; William L Kath; Adilson E Motter
Journal:  Nat Commun       Date:  2013       Impact factor: 14.919

3.  On the effects of memory and topology on the controllability of complex dynamical networks.

Authors:  Panagiotis Kyriakis; Sérgio Pequito; Paul Bogdan
Journal:  Sci Rep       Date:  2020-10-15       Impact factor: 4.379

4.  Functional observability and target state estimation in large-scale networks.

Authors:  Arthur N Montanari; Chao Duan; Luis A Aguirre; Adilson E Motter
Journal:  Proc Natl Acad Sci U S A       Date:  2022-01-04       Impact factor: 11.205

5.  Prevalence and scalable control of localized networks.

Authors:  Chao Duan; Takashi Nishikawa; Adilson E Motter
Journal:  Proc Natl Acad Sci U S A       Date:  2022-08-05       Impact factor: 12.779

6.  A practical guide to methodological considerations in the controllability of structural brain networks.

Authors:  Teresa M Karrer; Jason Z Kim; Jennifer Stiso; Ari E Kahn; Fabio Pasqualetti; Ute Habel; Danielle S Bassett
Journal:  J Neural Eng       Date:  2020-04-09       Impact factor: 5.379

7.  Intrinsic dynamics induce global symmetry in network controllability.

Authors:  Chen Zhao; Wen-Xu Wang; Yang-Yu Liu; Jean-Jacques Slotine
Journal:  Sci Rep       Date:  2015-02-12       Impact factor: 4.379

8.  Structural controllability and controlling centrality of temporal networks.

Authors:  Yujian Pan; Xiang Li
Journal:  PLoS One       Date:  2014-04-18       Impact factor: 3.240

9.  Diversified Control Paths: A Significant Way Disease Genes Perturb the Human Regulatory Network.

Authors:  Bingbo Wang; Lin Gao; Qingfang Zhang; Aimin Li; Yue Deng; Xingli Guo
Journal:  PLoS One       Date:  2015-08-18       Impact factor: 3.240

10.  Control capacity and a random sampling method in exploring controllability of complex networks.

Authors:  Tao Jia; Albert-László Barabási
Journal:  Sci Rep       Date:  2013       Impact factor: 4.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.