Image de couverture de livre

Koopman Form of Nonlinear Systems with Inputs

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Iacob, Lucian Cristian, Tóth, Roland, Schoukens, Maarten
Format:	Preprint
Publié:	2022
Sujets:	Systems and Control
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Exact Finite Koopman Embedding of Block-Oriented Polynomial Systems
par: Iacob, Lucian Cristian, et autres
Publié: (2025)

Space-Filling Input Design for Nonlinear State-Space Identification
par: Kiss, Máté, et autres
Publié: (2024)

Learning Koopman Models From Data Under General Noise Conditions
par: Iacob, Lucian Cristian, et autres
Publié: (2025)

Least Costly Space-Filling Experiment Design for the Identification of a Nonlinear System
par: Kiss, Máté, et autres
Publié: (2026)

On Space-Filling Input Design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach
par: Liu, Yuhan, et autres
Publié: (2025)

Nonparametric Control Koopman Operators
par: Bevanda, Petar, et autres
Publié: (2024)

Meta-State-Space Learning: An Identification Approach for Stochastic Dynamical Systems
par: Beintema, Gerben I., et autres
Publié: (2023)

Physics-Guided State-Space Model Augmentation Using Weighted Regularized Neural Networks
par: Liu, Yuhan, et autres
Publié: (2024)

Baseline Results for Selected Nonlinear System Identification Benchmarks
par: Champneys, Max D., et autres
Publié: (2024)

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

Orthogonal-by-construction augmentation of physics-based input-output models
par: Györök, Bendegúz M., et autres
Publié: (2025)

Efficient Learning of Affine and Rational Dependency LPV Models With Linear Fractional Representation
par: Drenth, Roel, et autres
Publié: (2026)

Learning-based augmentation of first-principle models: A linear fractional representation-based approach
par: Hoekstra, Jan H., et autres
Publié: (2026)

Koopman Data-Driven Predictive Control with Robust Stability and Recursive Feasibility Guarantees
par: de Jong, Thomas, 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)

Orthogonal projection-based regularization for efficient model augmentation
par: Györök, Bendegúz M., et autres
Publié: (2025)

Identification of Port-Hamiltonian Differential-Algebraic Equations from Input-Output Data
par: Hagelaars, N., et autres
Publié: (2026)

Computationally Efficient Sampling-Based Algorithm for Stability Analysis of Nonlinear Systems
par: Antal, Péter, et autres
Publié: (2024)

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

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

Scaled Relative Graph Analysis of General Interconnections of SISO Nonlinear Systems
par: Krebbekx, Julius P. J., et autres
Publié: (2025)

On Koopman Resolvents and Frequency Response of Nonlinear Systems
par: Susuki, Yoshihiko, et autres
Publié: (2026)

System identification of biophysical neuronal models
par: Burghi, Thiago B., et autres
Publié: (2020)

Encoder initialisation methods in the model augmentation setting
par: Hoekstra, J. H., et autres
Publié: (2026)

Modeling Nonlinear Control Systems via Koopman Control Family: Universal Forms and Subspace Invariance Proximity
par: Haseli, Masih, et autres
Publié: (2023)

Convex Equilibrium-Free Stability and Performance Analysis of Discrete-Time Nonlinear Systems
par: Koelewijn, Patrick J. W., et autres
Publié: (2024)

Feedback Identification of conductance-based models
par: Burghi, Thiago B., et autres
Publié: (2020)

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

Bilinear Input Modulation for Mamba: Koopman Bilinear Forms for Memory Retention and Multiplicative Computation
par: Fujii, Hiroki, et autres
Publié: (2026)

On the Existence of Koopman Linear Embeddings for Controlled Nonlinear Systems
par: Shang, Xu, et autres
Publié: (2026)

Nonlinear Bandwidth and Bode Diagrams based on Scaled Relative Graphs
par: Krebbekx, Julius P. J., et autres
Publié: (2025)

Unconstrained Parameterization of Stable LPV Input-Output Models: with Application to System Identification
par: Kon, Johan, et autres
Publié: (2024)

A Parallel-in-Time Newton's Method for Nonlinear Model Predictive Control
par: Iacob, Casian, et autres
Publié: (2024)

Willems' Fundamental Lemma for Nonlinear Systems with Koopman Linear Embedding
par: Shang, Xu, et autres
Publié: (2024)

Online Tracking with Predictions for Nonlinear Systems with Koopman Linear Embedding
par: Pai, Chih-Fan, et autres
Publié: (2026)

Deep Koopman Learning of Nonlinear Time-Varying Systems
par: Hao, Wenjian, et autres
Publié: (2022)

Measurements and System Identification for the Characterization of Smooth Muscle Cell Dynamics
par: Ozturk, Dilan, et autres
Publié: (2024)

On-the-fly Surrogation for Complex Nonlinear Dynamics
par: Olucha, E. Javier, et autres
Publié: (2025)

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

Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study
par: Schulze, Jan C., et autres
Publié: (2024)