Hysteresis-based supervisory control with application to non-pharmaceutical containment of COVID-19

Bin, M., Crisostomi, E., Ferraro, P., Murray-Smith, R. , Parisini, T., Shorten, R. and Stein, S. (2021) Hysteresis-based supervisory control with application to non-pharmaceutical containment of COVID-19. Annual Reviews in Control, (doi: 10.1016/j.arcontrol.2021.07.001) (Early Online Publication)

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Abstract

The recent COVID-19 outbreak has motivated an extensive development of non-pharmaceutical intervention policies for epidemics containment. While a total lockdown is a viable solution, interesting policies are those allowing some degree of normal functioning of the society, as this allows a continued, albeit reduced, economic activity and lessens the many societal problems associated with a prolonged lockdown. Recent studies have provided evidence that fast periodic alternation of lockdown and normal-functioning days may effectively lead to a good trade-off between outbreak abatement and economic activity. Nevertheless, the correct number of normal days to allocate within each period in such a way to guarantee the desired trade-off is a highly uncertain quantity that cannot be fixed a priori and that must rather be adapted online from measured data. This adaptation task, in turn, is still a largely open problem, and it is the subject of this work. In particular, we study a class of solutions based on hysteresis logic. First, in a rather general setting, we provide general convergence and performance guarantees on the evolution of the decision variable. Then, in a more specific context relevant for epidemic control, we derive a set of results characterizing robustness with respect to uncertainty and giving insight about how a priori knowledge about the controlled process may be used for fine-tuning the control parameters. Finally, we validate the results through numerical simulations tailored on the COVID-19 outbreak.

Item Type:Articles
Keywords:Hysteresis control, supervisory control, COVID-19.
Status:Early Online Publication
Refereed:Yes
Glasgow Author(s) Enlighten ID:Murray-Smith, Professor Roderick and Stein, Dr Sebastian
Authors: Bin, M., Crisostomi, E., Ferraro, P., Murray-Smith, R., Parisini, T., Shorten, R., and Stein, S.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Annual Reviews in Control
Publisher:Elsevier
ISSN:1367-5788
ISSN (Online):1872-9088
Published Online:13 August 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Annual Reviews in Control 2021
Publisher Policy:Reproduced under a Creative Commons licence

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
300982Exploiting Closed-Loop Aspects in Computationally and Data Intensive AnalyticsRoderick Murray-SmithEngineering and Physical Sciences Research Council (EPSRC)EP/R018634/1Computing Science