Data-driven control of complex networks

Giacomo Baggio, Danielle S. Bassett and Fabio Pasqualetti ()
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Giacomo Baggio: University of Padova
Danielle S. Bassett: University of Pennsylvania
Fabio Pasqualetti: University of California at Riverside

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract Our ability to manipulate the behavior of complex networks depends on the design of efficient control algorithms and, critically, on the availability of an accurate and tractable model of the network dynamics. While the design of control algorithms for network systems has seen notable advances in the past few years, knowledge of the network dynamics is a ubiquitous assumption that is difficult to satisfy in practice. In this paper we overcome this limitation, and develop a data-driven framework to control a complex network optimally and without any knowledge of the network dynamics. Our optimal controls are constructed using a finite set of data, where the unknown network is stimulated with arbitrary and possibly random inputs. Although our controls are provably correct for networks with linear dynamics, we also characterize their performance against noisy data and in the presence of nonlinear dynamics, as they arise in power grid and brain networks.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21554-0

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DOI: 10.1038/s41467-021-21554-0

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