AI RESEARCH
Learning Control-Affine Reduced-Order Models via Autoencoders
arXiv CS.LG
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ArXi:2606.05045v1 Announce Type: cross We present in this paper a framework for the identification of control-affine reduced-order models (ROMs). The proposed method utilizes autoencoders (AEs) to transform the high-dimensional states, and potentially the high-dimensional inputs, into reduced latent ones suitable for control-affine state-space dynamics. This is achieved by simultaneous