A direct parameterization ensures well-posedness of rational LPV-LFR models and supports joint estimation of the LPV plant and scheduling map from input-output data alone.
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Constraint-free LFR-based parametrizations ensure well-posedness and contraction-based stability for data-driven augmentation of physics models, paired with a non-smooth identification pipeline for automatic order selection.
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Efficient Learning of Affine and Rational Dependency LPV Models With Linear Fractional Representation
A direct parameterization ensures well-posedness of rational LPV-LFR models and supports joint estimation of the LPV plant and scheduling map from input-output data alone.
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Data-driven augmentation of first-principles models under constraint-free well-posedness and stability guarantees
Constraint-free LFR-based parametrizations ensure well-posedness and contraction-based stability for data-driven augmentation of physics models, paired with a non-smooth identification pipeline for automatic order selection.