Framework learns control-affine reduced-order models via jointly trained autoencoders on high-dimensional data, extended to sequence-based models and assessed on numerical examples for prediction and feedback linearization.
arXiv preprint arXiv:2405.01753 , year=
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Learning Control-Affine Reduced-Order Models via Autoencoders
Framework learns control-affine reduced-order models via jointly trained autoencoders on high-dimensional data, extended to sequence-based models and assessed on numerical examples for prediction and feedback linearization.