arXiv Machine Learning
Learning Neural Feedback Linearization for Data-driven Systems via Augmented Lagrangian
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arXiv Machine Learning
Jul 17
Modeling and Control of Deep Sign-Definite Dynamics with Application to Hybrid Powertrain Control
arXiv:2509. 19869v2 Announce Type: replace-cross Abstract: Data-driven control increasingly relies on deep models for complex systems whose first-principles models are difficult to obtain.
arXiv Machine Learning
Jun 24
Hessian-augmented Supervised Learning for Hamilton-Jacobi-Bellman PDEs
arXiv:2606. 23827v1 Announce Type: cross Abstract: A data-driven method is developed for approximating value functions in deterministic optimal control problems with nonlinear control-affine dynamics.
arXiv Machine Learning
Aug 10
Certified Feedforward Tracking for Unknown Nonlinear Systems via Invertible Neural Networks
arXiv:2608. 06419v1 Announce Type: cross Abstract: In this paper, we address the certification of datadriven feedforward control for periodic tracking of unknown nonlinear systems under partial state measurements.
Hugging Face Trending Papers
Jun 3
Learning Control-Affine Reduced-Order Models via Autoencoders
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.
arXiv Machine Learning
Jun 4
Learning Control-Affine Reduced-Order Models via Autoencoders
arXiv:2606. 05045v1 Announce Type: cross Abstract: We present in this paper a framework for the identification of control-affine reduced-order models (ROMs).
arXiv Machine Learning
Sep 1
Generalization as a robust performance property of learning-enabled dynamical systems
arXiv:2608.30431v1 Announce Type: cross Abstract: By focusing on algorithmic stability as a means of establishing out-of-sample bounds, we provide a system-theoretic interpretation of generalization...