论文标题
从稀缺数据中稳定动力学系统的上下文感知控制器推断
Context-aware controller inference for stabilizing dynamical systems from scarce data
论文作者
论文摘要
这项工作引入了一种数据驱动的控制方法，用于从稀缺数据稳定高维动力系统。提出的上下文感知控制器推断方法基于以下观察结果：控制器需要在不稳定的动态上进行本地行动以稳定系统。这意味着仅仅学习不稳定的动力学就足够了，通常将其限制在所有系统动力学的高维状态空间中的尺寸空间要少得多，因此很少有数据样本足以识别它们。数值实验表明，与传统的数据驱动的控制技术和增强学习的变体相比，上下文感知的控制器的推理从数据样本少的数量级稳定控制器学习。该实验进一步表明，与复杂物理学的数据筛分工程问题有关上下文感知的控制器推断的低数据需求特别有益，因此，在数据和培训成本方面，学习完整的系统动态通常是棘手的。
This work introduces a data-driven control approach for stabilizing high-dimensional dynamical systems from scarce data. The proposed context-aware controller inference approach is based on the observation that controllers need to act locally only on the unstable dynamics to stabilize systems. This means it is sufficient to learn the unstable dynamics alone, which are typically confined to much lower dimensional spaces than the high-dimensional state spaces of all system dynamics and thus few data samples are sufficient to identify them. Numerical experiments demonstrate that context-aware controller inference learns stabilizing controllers from orders of magnitude fewer data samples than traditional data-driven control techniques and variants of reinforcement learning. The experiments further show that the low data requirements of context-aware controller inference are especially beneficial in data-scarce engineering problems with complex physics, for which learning complete system dynamics is often intractable in terms of data and training costs.
