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arXiv 1902.10239math.DSmath.OCphysics.data-an
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用于控制的动力系统算子的数据驱动近似

Data-driven approximations of dynamical systems operators for control

Eurika Kaiser, J. Nathan Kutz, Steven L. Brunton

摘要:

Koopman和Perron Frobenius输运算子从根本上改变了我们处理动力系统的方式,甚至为强非线性动力学提供了线性表示。虽然这种线性表示对估计和控制有巨大的潜在好处,但传输算子是无限维的,这使得它们很难在数值上工作。获得这些算子的低维矩阵近似对于应用是至关重要的,并且动态模式分解已经迅速成为近似Koopman算子的标准数值算法。由于数据的日益丰富和DMD基于其在线性代数方面的简单框架的可扩展性的组合,相关方法已经看到了快速发展。在本章中,我们回顾了数据驱动的运输运营商控制特性的关键创新,提供了一个高层次和统一的视角。我们强调了稀疏性和控制方面的重要最新进展,并讨论了大数据和机器学习中的新兴方法。

英文摘要:

The Koopman and Perron Frobenius transport operators are fundamentally changing how we approach dynamical systems, providing linear representations for even strongly nonlinear dynamics. Although there is tremendous potential benefit of such a linear representation for estimation and control, transport operators are infinite-dimensional, making them difficult to work with numerically. Obtaining low-dimensional matrix approximations of these operators is paramount for applications, and the dynamic mode decomposition has quickly become a standard numerical algorithm to approximate the Koopman operator. Related methods have seen rapid development, due to a combination of an increasing abundance of data and the extensibility of DMD based on its simple framing in terms of linear algebra. In this chapter, we review key innovations in the data-driven characterization of transport operators for control, providing a high-level and unified perspective. We emphasize important recent developments around sparsity and control, and discuss emerging methods in big data and machine learning.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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