Abstract:
In this paper, a stochastic model predictive control (MPC) is proposed to design a non-pharmacutical policy to control and prevent the COVID-19 pandemic. The system dynamics of COVID-19 is described by a stochastic SEIHR model subject to practical constraints, and the model is proved to be feedback linearizable. A stochastic Control Lyapunov-Barrier Function (CLBF) is constructed for the feedback linearizable system. Constraints on hospitalized individuals are regarded as the unsafe region to construct the corresponding stochastic CLBF. In the proposed stochastic MPC, the stochastic CLBF constraints are applied to improve the overall performance on controlling and preventing the epidemic. Both theoretical proof and simulation results imply that, with the CLBF-based stochastic MPC, the proposed policy is effective in controlling and preventing COVID-19 pandemic.
