自适应采样让不稳定的系统也能安全收集训练数据
An adaptive data sampling strategy for stabilizing dynamical systems via controller inference
- 边控制边采样，实时防止系统失控
- 所需数据量比传统方法少一个数量级
- 适合难采样的极限状态与异常工况
从数据中学习稳定控制器是工程应用中的关键任务；然而，由于不稳定系统常导致轨迹迅速发散或混乱，获取有效数据极具挑战。本文提出一种自适应采样策略，在生成数据的同时稳定系统，避免数据采集过程中的不稳定性。在温和假设下，该方法可证明生成对稳定化任务信息丰富且规模最小的数据集。数值实验表明，采用该自适应采样方法进行控制器推断，所需数据样本数量比无引导数据生成减少一个数量级。结果表明，该方法为在边缘情况和极限状态等易失稳场景中实现系统稳定提供了可行路径。
原文摘要 · Abstract (English)
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.
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