Dynamic Mode Decomposition With Control Liouville Operators

• 成果类型: Article
• 署名作者: Rosenfeld, Joel A.; Kamalapurkar, Rushikesh
• 署名单位: State University System of Florida; University of South Florida; State University System of Florida; University of Florida
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2024.3419179
• 发表日期: 2024
• 页码: 8571-8586
• 关键词: Eigenvalues and eigenfunctions; kernel; trajectory; control systems; predictive models; Closed loop systems; Adaptive control; computational methods; dynamic mode decomposition; nonlinear system identification; Nonlinear systems; reduced order modeling

摘要

This article builds the theoretical foundations for dynamic mode decomposition (DMD) of control-affine dynamical systems by leveraging the theory of vector-valued reproducing kernel Hilbert spaces (RKHSs). Specifically, control Liouville operators and control occupation kernels are introduced to separate the drift dynamics from the input dynamics. A given feedback controller is represented through a multiplication operator, and a composition of the control Liouville operator and the multiplication operator is used to express the nonlinear closed-loop system as a linear total derivative operator on RKHSs. A spectral decomposition of a finite-rank representation of the total derivative operator yields a DMD of the closed-loop system. The DMD generates a model that can be used to predict the trajectories of the closed-loop system. For a large class of systems, the total derivative operator is shown to be compact provided that the domain and the range RKHSs are selected appropriately. The sequence of models, resulting from increasing-rank finite-rank representations of the compact total derivative operator, is shown to converge to the true system dynamics, provided that sufficiently rich data are available. Numerical experiments are included to demonstrate the efficacy of the developed technique.