Di Lauro Francesco, Kiss Istvan Zoltan, Rus Daniela, Della Santina Cosimo
Department of MathematicsUniversity of Sussex Brighton BN1 9QH U.K.
MIT Computer Science and Artificial Intelligence LaboratoryMassachusetts Institute of Technology Cambridge MA 02139 USA.
IEEE Control Syst Lett. 2020 Nov 19;5(4):1435-1440. doi: 10.1109/LCSYS.2020.3039322. eCollection 2021 Oct.
Many of the policies that were put into place during the Covid-19 pandemic had a common goal: to flatten the curve of the number of infected people so that its peak remains under a critical threshold. This letter considers the challenge of engineering a strategy that enforces such a goal using control theory. We introduce a simple formulation of the optimal flattening problem, and provide a closed form solution. This is augmented through nonlinear closed loop tracking of the nominal solution, with the aim of ensuring close-to-optimal performance under uncertain conditions. A key contribution of this letter is to provide validation of the method with extensive and realistic simulations in a Covid-19 scenario, with particular focus on the case of Codogno - a small city in Northern Italy that has been among the most harshly hit by the pandemic.
使感染人数曲线趋于平缓，以便其峰值保持在临界阈值以下。本文探讨了运用控制理论制定实现这一目标的策略所面临的挑战。我们提出了最优平缓问题的一种简单表述形式，并给出了封闭形式的解。通过对标称解进行非线性闭环跟踪对其进行了扩展，目的是在不确定条件下确保接近最优的性能。本文的一个关键贡献是在新冠疫情场景中通过广泛且现实的模拟对该方法进行验证，尤其关注科多尼奥的情况——意大利北部的一个小城，是受疫情冲击最严重的地区之一。
