GeKo
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Koopman operator learning for predictive control via Khatri-Rao kernel regression
arXiv:2606.02938v1 Announce Type: cross Abstract: This paper develops a data-driven realization of the generalized Koopman operator (GeKo), in which states and inputs are lifted independently and the dynamics are expressed as a tensor bilinear system. The first contribution is a time-sequenced multi-step Khatri-Rao kernel regression formulation that exposes the operator to evolved snapshots along trajectories rather than only single one-step pairs, which reduces compounded prediction error....