arXiv:2506. 01226v3 Announce Type: replace-cross Abstract: We study parameterizations of stabilizing nonlinear policies for learning-based control.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2607. 11122v1 Announce Type: cross Abstract: Implicit neural controllers (INCs) are static feedback laws that are evaluated through an algebraic fixed point {equation}; they include as special cases neural network controllers.
This paper presents a nonadaptive approach to robust nonlinear output regulation for general nonlinear systems with high relative degree in an output‑feedback setting. The design combines an input‑driven filter, a generic internal model, and a recursive backstepping law, transforming the regulation problem into robust input‑to‑state stabilization of an augmented error system. Under standard exosystem assumptions and a minimum‑phase input‑to‑state stability condition, the authors prove global asymptotic regulation and provide explicit inequalities for selecting design gains, demonstrating the method on a benchmark Duffing system.
arXiv:2606. 09047v1 Announce Type: cross Abstract: A classical universal stabilization formula offers the practitioner no design freedom: it is a single, parameter-free object.
arXiv:2609.22576v1 Announce Type: cross Abstract: Semidefinite programming (SDP) certificates for feedback systems containing deep neural networks (NNs) typically scale with the total number of neuro...
arXiv:2504. 01250v2 Announce Type: replace Abstract: This paper presents the Robust Recurrent Deep Network (R2DN), a scalable parameterization of robust recurrent neural networks for machine learning and data-driven control.
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...