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10.1016/j.arcontrol.2021.07.001

http://scihub22266oqcxt.onion/10.1016/j.arcontrol.2021.07.001
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34404974!8361045!34404974
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suck abstract from ncbi


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pmid34404974      Annu+Rev+Control 2021 ; 52 (ä): 508-522
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  • Hysteresis-based supervisory control with application to non-pharmaceutical containment of COVID-19 #MMPMID34404974
  • Bin M; Crisostomi E; Ferraro P; Murray-Smith R; Parisini T; Shorten R; Stein S
  • Annu Rev Control 2021[]; 52 (ä): 508-522 PMID34404974show ga
  • 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.
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