Characterizing the Predictive Accuracy of Dynamic Mode Decomposition for Data-Driven Control

Qiugang Lu; Sungho Shin; Victor M. Zavala

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Characterizing the Predictive Accuracy of Dynamic Mode Decomposition for Data-Driven Control

机译：对数据驱动控制的动态模式分解的预测精度表征

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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 impacts of model order and data availability. The analysis also establishes conditions under which DMD models can be made asymptotically exact. We numerically validate our theoretical results using a 2D diffusion system.

机译：动态模式分解（DMD）是一种多功能的方法，可以从数据构建低阶模型。基于此类模型的控制器设计任务需要估算和保证预测准确性。在这项工作中，我们提供了对DMD模型错误的理论分析，揭示了模型顺序和数据可用性的影响。分析还建立了DMD型号可以渐近精确的条件。我们使用2D扩散系统进行数字验证我们的理论结果。

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《IFAC PapersOnLine》 |2020年第2期|共6页
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Qiugang Lu; Sungho Shin; Victor M. Zavala;
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Dynamic mode decompositiondatacontrollow-order modelserror bounds;

机译：动态模式DecompositionDatacontrollut-Order ModelsError绑定;

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Characterizing the Predictive Accuracy of Dynamic Mode Decomposition for Data-Driven Control

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