Exact LTI Koopman models for nonlinear control systems require affine linear dynamics under controllability and coordinate inclusion assumptions.
Hamiltonian systems and transformation in Hilbert space
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Koopman operator regression on physics-simulated cloth data yields a linear surrogate model that enables efficient model predictive control for accurate dynamic folding trajectories on unseen poses in both simulation and real-robot experiments.
RC-Koopman uses reservoir computing as a stateful Koopman dictionary with spectral radius controlling temporal memory to achieve accurate and stable identification of nonlinear systems.
citing papers explorer
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Limitations of LTI Koopman Modeling for Nonlinear Control Systems
Exact LTI Koopman models for nonlinear control systems require affine linear dynamics under controllability and coordinate inclusion assumptions.
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Dynamic robotic cloth folding with efficient Koopman operator-based model predictive control
Koopman operator regression on physics-simulated cloth data yields a linear surrogate model that enables efficient model predictive control for accurate dynamic folding trajectories on unseen poses in both simulation and real-robot experiments.
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Koopman Identification of Nonlinear Systems via Reservoir Liftings
RC-Koopman uses reservoir computing as a stateful Koopman dictionary with spectral radius controlling temporal memory to achieve accurate and stable identification of nonlinear systems.