这篇论文用 P2L 方法从 400 个场景里只挑 2 个就搞定了 MPC 校准，风险界才 4.8%，而且置信度极高，做控制的人可以看看。
该论文展示了 Pick-to-Learn (P2L) 方法用于校准模型预测控制 (MPC) 策略。示例中飞机从起点到终点，需避开低连通区域，并应对不确定侧风。MPC 策略由两个超参数控制，P2L 从 400 个风场景中仅筛选出 2 个信息丰富的场景。校准后的策略在所有可用场景中均成功避开低连通区域，且理论风险界为 4.8%。该风险界以 1−10^−5 的置信度保证，相当于新风场景下进入低连通区域的概率上限。
Pick-to-Learn Calibration of an MPC Policy for an Origin-to-Destination Flight Problem
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.