Characterizing the Predictive Accuracy of Dynamic Mode Decomposition for Data-Driven Control
Systems and Control
2020-03-24 v2 Systems and Control
Optimization and Control
Abstract
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.
Cite
@article{arxiv.2003.01028,
title = {Characterizing the Predictive Accuracy of Dynamic Mode Decomposition for Data-Driven Control},
author = {Qiugang Lu and Sungho Shin and Victor M. Zavala},
journal= {arXiv preprint arXiv:2003.01028},
year = {2020}
}
Comments
6 pages, 5 figures