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

Abstract

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.