Web: http://arxiv.org/abs/2201.11640

Jan. 28, 2022, 2:11 a.m. | Petar Bevanda, Max Beier, Shahab Heshmati-Alamdari, Stefan Sosnowski, Sandra Hirche

cs.LG updates on arXiv.org arxiv.org

We propose a novel framework for learning linear time-invariant (LTI) models
for a class of continuous-time non-autonomous nonlinear dynamics based on a
representation of Koopman operators. In general, the operator is
infinite-dimensional but, crucially, linear. To utilize it for efficient LTI
control, we learn a finite representation of the Koopman operator that is
linear in controls while concurrently learning meaningful lifting coordinates.
For the latter, we rely on KoopmanizingFlows - a diffeomorphism-based
representation of Koopman operators. With such a learned …

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