发表机构
Department of Information Engineering, University of Brescia; Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano（信息工程系，布雷西亚大学; 电子、信息与生物工程系，米兰理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对飞机在有不确定侧风和低连通性区域时从起点到终点的飞行问题，用选择学习方法校准MPC策略，通过P2L程序从400个风场景数据中选超参数，所得策略能避低连通性区域，满足一定概率风险界限。
AI 中文摘要
本文阐述了应用于模型预测控制（MPC）策略校准的选择学习方法。虽然围绕一个特定示例展开，但旨在突出一种具有广泛适用性的方法。该示例涉及飞机在存在不确定侧风和应避开的低连通性区域的情况下从起点飞往终点。MPC策略由两个超参数参数化，通过P2L程序从数据中选择。从400个风实现（即场景）的数据集开始，P2L识别出仅包含两个信息丰富场景的最终压缩集。所得MPC策略在所有可用场景中避开低连通性区域，根据P2L理论，在置信水平1 - 10^(-5)下满足4.8%的概率风险界限，其中风险是在样本中未包含的新风实现下未来飞行中进入低连通性区域的概率。
英文摘要
This paper illustrates the Pick-to-Learn methodology applied to the calibration of a Model Predictive Control policy. While developed around a specific example, the presentation is meant to highlight a methodology of broad applicability. The example concerns an aircraft traveling from an origin point to a destination point in the presence of uncertain crosswinds and a low-connectivity zone that should be avoided. The MPC policy is parameterized by two hyperparameters, which are selected from data by the P2L procedure. Starting from a dataset of 400 wind realizations, also called scenarios, P2L identifies a final compression set containing only two informative scenarios. The resulting MPC policy avoids the low-connectivity zone on all available scenarios and, according to the P2L theory, satisfies a probabilistic risk bound of $4.8\%$ at confidence level $1-10^{-5}$, where the risk is the probability of entering the low-connectivity zone in a future flight under a new wind realization not included in the sample.
