论文标题
表征数据驱动控制的动态模式分解的预测精度
Characterizing the Predictive Accuracy of Dynamic Mode Decomposition for Data-Driven Control
论文作者
论文摘要
动态模式分解（DMD）是一种多功能方法，可以从数据中构建低阶模型。基于此类模型的控制器设计任务需要估算和保证预测精度。在这项工作中，我们提供了DMD模型错误的理论分析，该错误揭示了模型顺序和数据可用性的影响。该分析还建立了可以渐近地确切的DMD模型的条件。我们使用2D扩散系统验证结果。
Dynamic mode decomposition (DMD) is a versatile approach that enables the construction of low-order models from data. Controller design tasks based on such models require estimates and guarantees on predictive accuracy. In this work, we provide a theoretical analysis of DMD model errors that reveals impact of model order and data availability. The analysis also establishes conditions under which DMD models can be made asymptotically exact. We verify our results using a 2D diffusion system.
