Learning stabilizing controllers from data is an important task in engineering applications; however, collecting informative data is challenging because unstable systems often lead to rapidly growing or erratic trajectories. In this work, we propose an adaptive sampling scheme that generates data while simultaneously stabilizing the system to avoid instabilities during the data collection. Under mild assumptions, the approach provably generates data sets that are informative for stabilization and have minimal size. The numerical experiments demonstrate that controller inference with the novel adaptive sampling approach learns controllers with up to one order of magnitude fewer data samples than unguided data generation. The results show that the proposed approach opens the door to stabilizing systems in edge cases and limit states where instabilities often occur and data collection is inherently difficult.

翻译：从数据中学习稳定控制器是工程应用中的一项重要任务；然而，收集信息丰富的数据具有挑战性，因为不稳定系统通常会导致轨迹快速增长或难以预测。在本工作中，我们提出了一种自适应采样方案，该方案在生成数据的同时稳定系统，以避免数据收集过程中的不稳定性。在温和的假设下，该方法可证明生成对稳定化具有信息价值且规模最小的数据集。数值实验表明，采用这种新型自适应采样方法的控制器推断，其学习控制器所需的数据样本量比无引导的数据生成方法少一个数量级。结果表明，所提出的方法为在边缘情况和极限状态下稳定系统打开了大门，这些情况下不稳定性经常发生且数据收集本身就很困难。