Learning Feature Maps of the Koopman Operator: A Subspace Viewpoint

The Koopman operator was recently shown to be a useful method for nonlinear system identification and controller design. However, the scalability of current data-driven approaches is limited by the selection of feature maps. In this paper, we present a new data-driven framework for learning feature maps of the Koopman operator by introducing a novel separation method. The approach provides a flexible interface between diverse machine learning algorithms and well-developed linear subspace identification methods, as well as demonstrating a connection between the Koopman operator and observability. The proposed data-driven approach is tested by learning stable nonlinear dynamics generating hand-written characters, as well as a bilinear DC motor model.


Published in:
[Proceedings of the 2019 IEEE Conference on Decision and Control (CDC)]
Presented at:
The 58th IEEE Conference on Decision and Control, Nice, France, December 11th-13th 2019
Year:
Dec 11 2019
Publisher:
IEEE
Laboratories:




 Record created 2019-11-18, last modified 2019-12-05

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