针对SEIR仓室流行病模型定制的无终端要素模型预测控制
MPC without Terminal Ingredients Tailored to the SEIR Compartmental Epidemic Model
AI总结：
针对带状态和输入约束的SEIR流行病模型，提出无终端条件的定制MPC方案，证明其在适当二次成本和足够长预测时域下递归可行并渐近收敛到无病平衡点连续统，同时确定生存核为吸引域。
摘要：
我们考虑受状态和输入约束的SEIR仓室流行病模型（分别对感染个体比例设定上限，以及对允许的社交距离和隔离措施设定限制）。我们提出一种定制的无终端条件的模型预测控制（MPC）方案。我们严格证明了对于适当设计的二次运行成本和足够长的预测时域（预测窗口），MPC闭环具有递归可行性，并渐近收敛到无病平衡点的连续统。此外，我们建立了生存核（又称容许集）作为该平衡点连续统的吸引域。
英文摘要：
We consider the SEIR compartmental epidemic model subject to state and input constraints (a cap on the proportion of infectious individuals and limits on the allowed social distancing and quarantining measures, respectively). We present a tailored model predictive control (MPC) scheme without terminal conditions. We rigorously show recursive feasibility and asymptotic convergence of the MPC closed loop to the continuum of disease-free equilibrium points for suitably designed quadratic running cost and a sufficiently long prediction horizon (forecast window). Moreover, we establish the viability kernel (a.k.a. the admissible set) as a domain of attraction of the continuum of equilibria.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
