Vishnu Varadan
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Office: ME C2 409
EPFL › STI › IGM › SCI-STI-AK
Website: https://www.epfl.ch/labs/ddmac/
2025
Data-driven Feedback Linearization in the Koopman Observable Manifold
This paper proposes a novel data-driven approach for feedback linearization of nonlinear control-affine systems by leveraging the Koopman operator framework. We establish theoretical connections between feedback linearization on the original state manifold and the higher-dimensional Koopman observable manifold using concepts from system immersion. For systems with exact Koopman bilinear representations, we provide closed-form solutions to the feedback linearization problem without solving partial differential equations. When exact bilinear representations are not available, we develop an approximate method based on singular value decomposition that converges to the exact solution as the observables are enriched. The simulation results and numerical examples demonstrate the effectiveness of the approach.
2025Efficient Multi-step Identification of Koopman Operators
This paper proposes computationally efficient methods for identifying Koopman operators using multi-step output-error minimization. The two complementary approaches proposed in the paper trade-off between model accuracy and computational efficiency. Then, we provide an extensive comparison of the proposed methods to the standard Extended Dynamic Mode Decomposition (EDMD) approach through numerical examples. The results demonstrate the advantages of the proposed methods in terms of open and closed-loop performance, while remaining computationally efficient.
2025