论文标题
扩散摩尼子模型预测控制
Diffusing-Horizon Model Predictive Control
论文作者
论文摘要
我们分析了一种模型预测控制（MPC）的时期策略，该策略称为散射式Horizon MPC。该策略旨在克服与跨越多个时间尺度的最佳控制问题相关的计算挑战。粗糙的方法使用时间离散网格，随着时间的推移向前移动，该网格变得更加稀疏。该设计的激励是由最近确立的最佳控制问题的特性，称为敏感性的指数衰减。该属性指出，随着时间的推移向后移动，将来的参数扰动的影响呈指数衰减。我们建立了该属性具有线性动力和成本的受限MPC公式的条件。此外，我们表明所提出的粗化方案可以作为MPC问题的参数扰动施放，因此可以将指数衰减状态施加。我们使用带有实际数据的加热，通风和空调厂案例研究来证明所提出的方法。具体而言，我们表明，计算时间可以减少两个数量级，同时将闭环成本仅增加3％。
We analyze a time-coarsening strategy for model predictive control (MPC) that we call diffusing-horizon MPC. This strategy seeks to overcome the computational challenges associated with optimal control problems that span multiple timescales. The coarsening approach uses a time discretization grid that becomes exponentially more sparse as one moves forward in time. This design is motivated by a recently established property of optimal control problems that is known as exponential decay of sensitivity. This property states that the impact of a parametric perturbation at a future time decays exponentially as one moves backward in time. We establish conditions under which this property holds for a constrained MPC formulation with linear dynamics and costs. Moreover, we show that the proposed coarsening scheme can be cast as a parametric perturbation of the MPC problem and thus the exponential decay condition holds. We use a heating, ventilation, and air conditioning plant case study with real data to demonstrate the proposed approach. Specifically, we show that computational times can be reduced by two orders of magnitude while increasing the closed-loop cost by only 3%.
