论文标题
通过稀疏资源分配对扩散过程的模型预测控制
Model Predictive Control of Spreading Processes via Sparse Resource Allocation
论文作者
论文摘要
在本文中，我们提出了一种模型预测控制（MPC）方法，用于通过大规模网络的实时干预扩散过程（例如流行病和野火）。目标是每次分配预算的资源，以最大程度地降低未发现疫情的风险，即爆发概率的产物和该爆发的影响。通过使用动态编程放松，将MPC控制器重新构成凸优化问题，尤其是指数圆锥编程。我们还为闭环风险提供了足够的条件，以渐近减少，并一种估计风险单调降低何时的上限的方法。为野火示例提供了数值结果。
In this paper, we propose a model predictive control (MPC) method for real-time intervention of spreading processes, such as epidemics and wildfire, over large-scale networks. The goal is to allocate budgeted resources each time step to minimize the risk of an undetected outbreak, i.e. the product of the probability of an outbreak and the impact of that outbreak. By using dynamic programming relaxation, the MPC controller is reformulated as a convex optimization problem, in particular an exponential cone programming. We also provide sufficient conditions for the closed-loop risks to asymptotically decrease and a method to estimate the upper bound of when the risk will monotonically decrease. Numerical results are provided for a wildfire example.
