arXiv Machine Learning

Learning Neural Feedback Linearization for Data-driven Systems via Augmented Lagrangian

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.

By Teruki Kato, Ryotaro Shima, Kenji Kashima

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.

By Mat\'ias G\'omez-Aedo, Behzad Azmi, Yuyang Huang, Dante Kalise, Karl Kunisch

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.

By Berk Altiner, Rajasree Sarkar, Arunava Banerjee, Zongxuan Sun, Kenneth Kim

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.

diffusion

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).

By Ali Mjalled, Martin M\"onnigmann

diffusion

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...

By Filippo Fabiani

arXiv Machine Learning

Sep 4

Linearized subspace refinement framework to expose hidden accuracy in trained neural networks

The paper introduces Linearized Subspace Refinement (LSR), a post‑training framework that uses the local linearized model of a trained neural network to compute a low‑dimensional correction via a reduced least‑squares problem. LSR is architecture‑agnostic and improves accuracy across tasks such as function approximation, operator learning, physics‑informed fine‑tuning, and noisy inverse problems, often achieving order‑of‑magnitude error reductions. The method reveals that standard training can leave significant accuracy plateaus due to numerical ill‑conditioning, and it offers a subspace rank that balances correction strength, stability, and noise sensitivity.

By Wenbo Cao, Weiwei Zhang

fine-tuning

arXiv Machine Learning

Jul 1

Machine Learning-based Feedback Linearization Control of Quadrotor Subject to Unmodeled Dynamics

arXiv:2606. 31199v1 Announce Type: cross Abstract: The control of agile quadrotors in dynamic and uncertain environments remains an open area of investigation to this day, particularly when the complete system dynamics are partially known or highly nonlinear.

By Amos Alwala, Gabriel da Silva Lima, Wallace Moreira Bessa

arXiv Machine Learning

Aug 26

A Robust Task-Level Control Architecture for Learned Dynamical Systems

arXiv:2511.09790v2 Announce Type: replace-cross Abstract: Dynamical system (DS)-based learning from demonstration (LfD) is a powerful tool for generating motion plans in the operation ('task') space...

By Eshika Pathak, Ahmed Aboudonia, Sandeep Banik, Naira Hovakimyan

robotics

arXiv AI

Jun 9

Hybrid Neural Network and Conventional Controller Approach for Robust Control of Highly Unstable Systems: Application to Tilt-Rotor Control

arXiv:2606. 08714v1 Announce Type: cross Abstract: Multirotors are widely used in applications ranging from surveillance to precision agriculture, yet conventional designs remain limited by their under-actuation.

By Ali Kafili Gavgani, Amin Talaeizadeh, Aria Alasty, Hossein Nejat Pishkenari

llms

arXiv Machine Learning

Jun 4

Certified Neural Approximations of Nonlinear Dynamics

arXiv:2505. 15497v3 Announce Type: replace Abstract: Neural networks hold great potential to act as approximate models of nonlinear dynamical systems, with the resulting neural approximations enabling verification and control of such systems.

By Frederik Baymler Mathiesen, Nikolaus Vertovec, Francesco Fabiano, Luca Laurenti, Alessandro Abate

benchmarks safety

arXiv Machine Learning

Jun 2

Symmetric Hermite quadrature-based balanced truncation for learning linear dynamical systems from derivative data

arXiv:2606. 00298v1 Announce Type: cross Abstract: Data-driven reduced-order modeling is an essential component in the computer-aided design of control systems.

By Sean Reiter, Steffen W. R. Werner

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