Data-driven model prediction and optimal control for interventional policy of a class of susceptible-infectious-removed dynamics with COVID-19 data

被引：0

作者：

Treesatayapun C. ^{[1
]}

机构：

[1] Department of Robotic and Advanced Manufacturing, CINVESTAV-IPN, Ramos Arizpe

来源：

Advanced Control for Applications: Engineering and Industrial Systems | 2022年 / 4卷 / 04期

关键词：

COVID-19; discrete-time SEAICR model; fuzzy rules emulated networks; interventional policy; optimal control;

D O I：

10.1002/adc2.115

中图分类号：

学科分类号：

摘要：

Adaptive optimal-control and model prediction are proposed for a class of susceptible-infectious-removed dynamics according to the COVID-19 data. From the practical point of view, data sets of COVID-19 pandemics are daily collected and presented in a discrete-time sequence. Therefore, the discrete-time mathematical model of COVID-19 pandemics is formulated in this work. By developing the time-varying transmission rate, the model's accuracy is significantly contributed to the actual data of the COVID-19 pandemic. Furthermore, the interventional policy is derived by the proposed optimal controller when the closed-loop performance is guaranteed by theoretical aspects and numerical results. © 2022 John Wiley & Sons Ltd.

引用

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