Sparse control to prevent Black Swan clustering in collective dynamics

Benedetto Piccoli, Nastassia Pouradier Duteil, Emmanuel Trélat

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we elaborate control strategies to prevent clustering effects, as opposed to numerous works in the literature seeking to control multi-agent systems to achieve consensus. We consider general controlled collective dynamics and show how the group variance should be replaced by an entropy-type functional to measure clustering. Then we focus on Hegselmann-Krause type models and propose sparse declustering controls for the discrete system as well as for its mean-field limit. The behavior or the interaction function at zero and at infinity characterizes whether clustering can be avoided by controlling the system. Such results include the description of black holes (where complete collapse to consensus is not avoidable), safety regions (where the control can keep the system far from clustering), basins of attraction (attractive zones around the clustering manifold) and collapse prevention.

Original languageEnglish (US)
Title of host publication2018 Annual American Control Conference, ACC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781538654286
StatePublished - Aug 9 2018
Event2018 Annual American Control Conference, ACC 2018 - Milwauke, United States
Duration: Jun 27 2018Jun 29 2018

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2018 Annual American Control Conference, ACC 2018
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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