Linear Matrix Inequality Approaches to Koopman Operator Approximation
Systems and Control
2021-10-20 v2 Machine Learning
Systems and Control
Dynamical Systems
Abstract
The regression problem associated with finding a matrix approximation of the Koopman operator from data is considered. The regression problem is formulated as a convex optimization problem subject to linear matrix inequality (LMI) constraints. Doing so allows for additional LMI constraints to be incorporated into the regression problem. In particular, asymptotic stability constraints, regularization using matrix norms, and even regularization using system norms can be easily incorporated into the regression problem.
Keywords
Cite
@article{arxiv.2102.03613,
title = {Linear Matrix Inequality Approaches to Koopman Operator Approximation},
author = {Steven Dahdah and James Richard Forbes},
journal= {arXiv preprint arXiv:2102.03613},
year = {2021}
}
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13 pages