Image de couverture de livre

Learning Stable and Robust Linear Parameter-Varying State-Space Models

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Verhoek, Chris, Wang, Ruigang, Tóth, Roland
Format:	Preprint
Publié:	2023
Sujets:	Systems and Control Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Direct Data-Driven State-Feedback Control of Linear Parameter-Varying Systems
par: Verhoek, Chris, et autres
Publié: (2022)

Data-driven Dissipativity Analysis of Linear Parameter-Varying Systems
par: Verhoek, Chris, et autres
Publié: (2023)

A Linear Parameter-Varying Approach to Data Predictive Control
par: Verhoek, Chris, et autres
Publié: (2023)

Direct Data‐Driven State‐Feedback Control of Linear Parameter‐Varying Systems
par: Chris Verhoek, et autres
Publié: (2025)

Kernel-based multi-step predictors for data-driven analysis and control of nonlinear systems through the velocity form
par: Verhoek, Chris, et autres
Publié: (2024)

On the reduction of Linear Parameter-Varying State-Space models
par: Olucha, E. Javier, et autres
Publié: (2024)

Learning Surrogate LPV State-Space Models with Uncertainty Quantification
par: Olucha, E. Javier, et autres
Publié: (2026)

Learning Stable and Passive Neural Differential Equations
par: Cheng, Jing, et autres
Publié: (2024)

Direct data-driven LPV control of nonlinear systems: An experimental result
par: Verhoek, Chris, et autres
Publié: (2022)

Direct data-driven interpolation and approximation of linear parameter-varying system trajectories
par: Verhoek, Chris, et autres
Publié: (2025)

On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks
par: Barbara, Nicholas H., et autres
Publié: (2024)

Identification of Shifted‐Affine Linear Parameter‐Varying Models in the Errors‐In‐Variables Setting
par: Leander Hemelhof, et autres
Publié: (2026)

A Direct State-Space Realization of Discrete-Time Linear Parameter-Varying Input-Output Models
par: Kon, Johan, et autres
Publié: (2025)

Robustly Invertible Nonlinear Dynamics and the BiLipREN: Contracting Neural Models with Contracting Inverses
par: Zhang, Yurui, et autres
Publié: (2025)

Learning Reduced-Order Linear Parameter-Varying Models of Nonlinear Systems
par: Koelewijn, Patrick J. W., et autres
Publié: (2023)

Learning-based model augmentation with LFRs
par: Hoekstra, Jan H., et autres
Publié: (2024)

A behavioral approach for LPV data-driven representations
par: Verhoek, Chris, et autres
Publié: (2024)

Decoupling parameter variation from noise: Biquadratic Lyapunov forms in data-driven LPV control
par: Verhoek, Chris, et autres
Publié: (2024)

RobustNeuralNetworks.jl: a Package for Machine Learning and Data-Driven Control with Certified Robustness
par: Barbara, Nicholas H., et autres
Publié: (2023)

React to Surprises: Stable-by-Design Neural Feedback Control and the Youla-REN
par: Barbara, Nicholas H., et autres
Publié: (2025)

Negative Imaginary Neural ODEs: Learning to Control Mechanical Systems with Stability Guarantees
par: Shi, Kanghong, et autres
Publié: (2025)

Time-Varying Deep State Space Models for Sequences with Switching Dynamics
par: Karilanova, Sanja, et autres
Publié: (2026)

Efficient identification of linear, parameter-varying, and nonlinear systems with noise models
par: Bemporad, Alberto, et autres
Publié: (2025)

Identifying Large-Scale Linear Parameter Varying Systems with Dynamic Mode Decomposition Methods
par: Jordanou, Jean Panaioti, et autres
Publié: (2025)

Direct data-driven control with signal temporal logic specifications
par: van Huijgevoort, Birgit C., et autres
Publié: (2023)

Direct data-driven state-feedback control of general nonlinear systems
par: Verhoek, Chris, et autres
Publié: (2023)

Automated Linear Parameter-Varying Modeling of Nonlinear Systems: A Global Embedding Approach
par: Olucha, E. Javier, et autres
Publié: (2025)

Unifying Sequential Quadratic Programming and Linear-Parameter-Varying Algorithms for Real-Time Model Predictive Control
par: Floch, Kristóf, et autres
Publié: (2025)

Space-Filling Regularization for Robust and Interpretable Nonlinear State Space Models
par: Klein, Hermann, et autres
Publié: (2025)

Adversarial Robustness of Deep State Space Models for Forecasting
par: Anand, Sribalaji C., et autres
Publié: (2026)

R2DN: Scalable Parameterization of Contracting and Lipschitz Recurrent Deep Networks
par: Barbara, Nicholas H., et autres
Publié: (2025)

Lipschitz-bounded 1D convolutional neural networks using the Cayley transform and the controllability Gramian
par: Pauli, Patricia, et autres
Publié: (2023)

State Derivative Normalization for Continuous-Time Deep Neural Networks
par: Weigand, Jonas, et autres
Publié: (2024)

Data-driven augmentation of first-principles models under constraint-free well-posedness and stability guarantees
par: Györök, Bendegúz, et autres
Publié: (2026)

Improved Robustness of Deep Reinforcement Learning for Control of Time-Varying Systems by Bounded Extremum Seeking
par: Saxena, Shaifalee, et autres
Publié: (2025)

Stable Linear Subspace Identification: A Machine Learning Approach
par: Di Natale, Loris, et autres
Publié: (2023)

Layer-Adaptive State Pruning for Deep State Space Models
par: Gwak, Minseon, et autres
Publié: (2024)

Graph Neural Networks on Factor Graphs for Robust, Fast, and Scalable Linear State Estimation with PMUs
par: Kundacina, Ognjen, et autres
Publié: (2023)

Recursive Gaussian Process State Space Model
par: Zheng, Tengjie, et autres
Publié: (2024)

CLT-Optimal Parameter Error Bounds for Linear System Identification
par: Zhou, Yichen, et autres
Publié: (2026)