Copertina

Neural Port-Hamiltonian Models for Nonlinear Distributed Control: An Unconstrained Parametrization Approach

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zakwan, Muhammad, Ferrari-Trecate, Giancarlo
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Systems and Control Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Neural Distributed Controllers with Port-Hamiltonian Structures
di: Zakwan, Muhammad, et al.
Pubblicazione: (2024)

L2RU: a Structured State Space Model with prescribed L2-bound
di: Massai, Leonardo, et al.
Pubblicazione: (2025)

Neural Exponential Stabilization of Control-affine Nonlinear Systems
di: Zakwan, Muhammad, et al.
Pubblicazione: (2024)

Robust Convolution Neural ODEs via Contractivity-promoting regularization
di: Zakwan, Muhammad, et al.
Pubblicazione: (2025)

Controller Design for Structured State-space Models via Contraction Theory
di: Zakwan, Muhammad, et al.
Pubblicazione: (2026)

Stable Linear Subspace Identification: A Machine Learning Approach
di: Di Natale, Loris, et al.
Pubblicazione: (2023)

Learning to Boost the Performance of Stable Nonlinear Systems
di: Furieri, Luca, et al.
Pubblicazione: (2024)

MAD: A Magnitude And Direction Policy Parametrization for Stability Constrained Reinforcement Learning
di: Furieri, Luca, et al.
Pubblicazione: (2025)

Maximum likelihood inference for high-dimensional problems with multiaffine variable relations
di: Brouillon, Jean-Sébastien, et al.
Pubblicazione: (2024)

Parametrizations of All Stable Closed-loop Responses: From Theory to Neural Network Control Design
di: Galimberti, Clara Lucía, et al.
Pubblicazione: (2024)

SIMBa: System Identification Methods leveraging Backpropagation
di: Di Natale, Loris, et al.
Pubblicazione: (2023)

Learning Subsystem Dynamics in Nonlinear Systems via Port-Hamiltonian Neural Networks
di: van Otterdijk, G. J. E., et al.
Pubblicazione: (2024)

Unconstrained learning of networked nonlinear systems via free parametrization of stable interconnected operators
di: Massai, Leonardo, et al.
Pubblicazione: (2023)

On the Global Optimality of Linear Policies for Sinkhorn Distributionally Robust Linear Quadratic Control
di: Cescon, Riccardo, et al.
Pubblicazione: (2025)

Data-driven Distributionally Robust Control Based on Sinkhorn Ambiguity Sets
di: Cescon, Riccardo, et al.
Pubblicazione: (2025)

Closing the Gap to Quadratic Invariance: a Regret Minimization Approach to Optimal Distributed Control
di: Martinelli, Daniele, et al.
Pubblicazione: (2023)

Sinkhorn Ambiguity Sets for Distributionally Robust Control: Convexity, Weak Compactness, and Tractability
di: Cescon, Riccardo, et al.
Pubblicazione: (2026)

Resilient AFE Drive Control using Neural Networks with Tracking Guarantees
di: Kirsch, Nicolas, et al.
Pubblicazione: (2025)

Constrained Performance Boosting Control for Nonlinear Systems via ADMM
di: Giacomelli, Gianluca, et al.
Pubblicazione: (2025)

Discrete-time Contraction-based Control of Nonlinear Systems with Parametric Uncertainties using Neural Networks
di: Wei, Lai, et al.
Pubblicazione: (2021)

Hierarchical Control for Vehicle Repositioning in Autonomous Mobility on Demand Systems
di: Zhu, Pengbo, et al.
Pubblicazione: (2024)

Stability-Preserving Online Adaptation of Neural Closed-loop Maps
di: Saccani, Danilo, et al.
Pubblicazione: (2026)

Neural Identification of Feedback-Stabilized Nonlinear Systems
di: Boroujeni, Mahrokh G., et al.
Pubblicazione: (2025)

Safety-Aware Performance Boosting for Constrained Nonlinear Systems
di: Saccani, Danilo, et al.
Pubblicazione: (2026)

System Identification for Dynamic Modeling of Large Steering Angle Vehicles
di: Petri, Tobias, et al.
Pubblicazione: (2025)

A Coverage Control-based Idle Vehicle Rebalancing Approach for Autonomous Mobility-on-Demand Systems
di: Zhu, Pengbo, et al.
Pubblicazione: (2023)

Data-Driven Optimal Distributed Controller Synthesis via Spatial Regret
di: Gupta, Vaibhav, et al.
Pubblicazione: (2026)

Co-Learning Port-Hamiltonian Systems and Optimal Energy-Shaping Control
di: Kamboj, Ankur, et al.
Pubblicazione: (2026)

Structure- and Stability-Preserving Learning of Port-Hamiltonian Systems
di: Nguyen, Binh, et al.
Pubblicazione: (2026)

Distributionally Robust Infinite-horizon Control: from a pool of samples to the design of dependable controllers
di: Brouillon, Jean-Sébastien, et al.
Pubblicazione: (2023)

MAKO: Meta-Adaptive Koopman Operators for Learning-based Model Predictive Control of Parametrically Uncertain Nonlinear Systems
di: Han, Minghao, et al.
Pubblicazione: (2025)

Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks
di: Neary, Cyrus, et al.
Pubblicazione: (2024)

Moving horizon partition-based state estimation of large-scale systems -- Revised version
di: Farina, Marcello, et al.
Pubblicazione: (2024)

Boosting the transient performance of reference tracking controllers with neural networks
di: Kirsch, Nicolas, et al.
Pubblicazione: (2025)

En Route Path-planning for Partially Occupied Vehicles in Ride-pooling Systems
di: Zhu, Pengbo, et al.
Pubblicazione: (2025)

Physics-Constrained Learning for PDE Systems with Uncertainty Quantified Port-Hamiltonian Models
di: Tan, Kaiyuan, et al.
Pubblicazione: (2024)

Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks
di: Heilig, Simon, et al.
Pubblicazione: (2024)

Learning Passive Continuous-Time Dynamics with Multistep Port-Hamiltonian Gaussian Processes
di: Leung, Chi Ho, et al.
Pubblicazione: (2025)

Parametric Interpolation of Dynamic Mode Decomposition for Predicting Nonlinear Systems
di: Chakrabarti, Ananda, et al.
Pubblicazione: (2026)

Neural Operators for Predictor Feedback Control of Nonlinear Delay Systems
di: Bhan, Luke, et al.
Pubblicazione: (2024)