发表机构
University of Colorado, Boulder; Eindhoven University of Technology（科罗拉多大学博尔德分校; 埃因霍温理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究在数据驱动控制中利用平衡流形先验信息，证明其能降低间接辨识的估计方差并提升控制性能，且直接方法无需显式辨识即可获益。
AI 中文摘要
许多数据驱动控制方法基于被控系统完全未知的假设，因此没有利用可用或易于推断的先验信息。与这一观点相反，本文分析了利用系统的平衡子空间来指导直接和间接线性二次调节的影响。对于间接情况，我们展示了在辨识问题中加入对平衡子空间的约束如何改变学习模型的统计特性。具体而言，我们表明强制与平衡子空间的一致性会导致估计器方差的减小，进而提升基于模型的控制性能。在直接情况下，我们展示了如何利用这一先验信息，在无需显式辨识步骤的情况下获得对被控系统的洞察。这些结果得到了数值和实验证据的支持，展示了在数据驱动控制中显式利用平衡流形作为先验的优势。
英文摘要
By hinging on the assumption that a system to be controlled is fully unknown, many data-driven control approaches do not leverage available or readily inferable priors. In contrast to this viewpoint, this paper analyzes the impact of using the system's equilibrium subspace to inform direct and indirect linear quadratic regulation. For the indirect case, we show how including a constraint on the equilibrium subspace in the identification problem changes the statistical properties of the learned model. In particular, we show that enforcing consistency with respect to the equilibrium subspace leads to a reduction in the estimator variance that, in turn, enhances model-based control performance. In the direct case, we show how this prior can be leveraged to gain insight into the controlled system without requiring an explicit identification step. These results are supported by both numerical and experimental evidence, showcasing the advantages of explicitly leveraging the equilibrium manifold as a prior in data-driven control.
CommentsAccepted at the 65th IEEE Conference on Decision and Control (CDC 2026)
