Supervised Learning of Lyapunov Functions Using Laplace Averages of Approximate Koopman Eigenfunctions
نویسندگان
چکیده
Modern data-driven techniques have rapidly progressed beyond modelling and systems identification, with a growing interest in learning high-level dynamical properties of system, such as safe-set invariance, reachability, input-to-state stability etc. In this paper, we propose novel supervised Deep Learning technique for constructing Lyapunov certificates, by leveraging Koopman Operator theory-based numerical tools (Extended Dynamic Mode Decomposition Generalized Laplace Analysis) to robustly efficiently generate explicit ground truth data training. This is stark contrast existing methods where the loss functions plainly penalize condition violation absence labelled direct regression. Furthermore, our approach leads linear parameterization candidate terms stable eigenfunctions operator, making them more interpretable compared standard DNN-based architecture. We demonstrate validate numerically using 2-dimensional 10-dimensional examples.
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ژورنال
عنوان ژورنال: IEEE Control Systems Letters
سال: 2023
ISSN: ['2475-1456']
DOI: https://doi.org/10.1109/lcsys.2023.3291657