A practical input-to-state stability certificate for Koopman learning control is derived, separating prediction residuals from selected-channel margins and projection residuals.
Müller, and Frank Allgöwer
3 Pith papers cite this work, alongside 739 external citations. Polarity classification is still indexing.
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representative citing papers
A behavioral data-driven framework based on Willems' lemma generates optimal open-loop slewing trajectories for rotary cranes that suppress load sway using limited input-output data and convex optimization.
A differentiable framework integrates function encoder-based neural ODEs with predictive control to enable zero-shot adaptation of explicit policies across families of nonlinear systems.
citing papers explorer
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Input-to-State Stability Certification via Projection Residuals for Koopman Learning Control of Nonlinear Repetitive Systems
A practical input-to-state stability certificate for Koopman learning control is derived, separating prediction residuals from selected-channel margins and projection residuals.
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Behavioral Data-Driven Optimal Trajectory Generation for Rotary Cranes
A behavioral data-driven framework based on Willems' lemma generates optimal open-loop slewing trajectories for rotary cranes that suppress load sway using limited input-output data and convex optimization.
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Zero-Shot Function Encoder-Based Differentiable Predictive Control
A differentiable framework integrates function encoder-based neural ODEs with predictive control to enable zero-shot adaptation of explicit policies across families of nonlinear systems.