用少量典型风况数据，自动调优飞行控制策略，确保安全穿越弱信号区。
Pick-to-Learn Calibration of an MPC Policy for an Origin-to-Destination Flight Problem
- 从400个风况中筛选出2个关键场景，通过数据驱动方法优化控制参数。
- 新策略在所有测试风况下均避开弱信号区，风险低于4.8%。
- 适合需要高可靠性、数据稀疏环境下的自主飞行系统设计者。
本文展示了Pick-to-Learn（P2L）方法在模型预测控制（MPC）策略校准中的应用。以飞机在存在不确定侧风和需避开的低连通性区域条件下从起点飞往终点为例，MPC策略由两个超参数决定，通过P2L流程从400个风况样本（称为情景）中选择最优参数。P2L最终识别出仅包含两个具有信息量的情景的压缩集。所得到的MPC策略在所有可用情景中均避开低连通性区域，且根据P2L理论，在置信度1−10⁻⁵下满足未来新风况下进入该区域的概率风险不超过4.8%。
原文摘要 · Abstract (English)
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.
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