Marco Forgione

IEEE Control. Syst. Lett., 2025

Physics-Informed Neural Networks for Hidden Insulin Dynamics Estimation from Glucose Data.

[BibT_eX]

[DOI]

Proceedings of the Artificial Intelligence in Medicine - 23rd International Conference, 2025

2024

One controller to rule them all.

[BibT_eX]

[DOI]

CoRR, 2024

Integrating Reinforcement Learning with Foundation Models for Autonomous Robotics: Methods and Perspectives.

[BibT_eX]

[DOI]

CoRR, 2024

Enhanced Transformer architecture for in-context learning of dynamical systems.

[BibT_eX]

[DOI]

CoRR, 2024

RoboMorph: In-Context Meta-Learning for Robot Dynamics Modeling.

[BibT_eX]

[DOI]

Proceedings of the 21st International Conference on Informatics in Control, 2024

Synthetic data generation for system identification: leveraging knowledge transfer from similar systems.

[BibT_eX]

[DOI]

Proceedings of the 63rd IEEE Conference on Decision and Control, 2024

Model order reduction of deep structured state-space models: A system-theoretic approach.

[BibT_eX]

[DOI]

Proceedings of the 63rd IEEE Conference on Decision and Control, 2024

A Computationally Efficient Deep Learning-Based Surrogate Model of Prediabetes Progression.

[BibT_eX]

[DOI]

Lea Multerer

Stefano Toniolo

Sandra Mitrovic

Maria Concetta Palumbo

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2024

2023

On the adaptation of recurrent neural networks for system identification.

[BibT_eX]

[DOI]

Autom., September, 2023

From System Models to Class Models: An In-Context Learning Paradigm.

[BibT_eX]

[DOI]

Filippo Pura

IEEE Control. Syst. Lett., 2023

In-context learning of state estimators.

[BibT_eX]

[DOI]

CoRR, 2023

On the adaptation of in-context learners for system identification.

[BibT_eX]

[DOI]

Filippo Pura

CoRR, 2023

In-context learning for model-free system identification.

[BibT_eX]

[DOI]

Filippo Pura

CoRR, 2023

Neural State-Space Models: Empirical Evaluation of Uncertainty Quantification.

[BibT_eX]

[DOI]

CoRR, 2023

2022

Learning Dynamical Systems From Quantized Observations: A Bayesian Perspective.

[BibT_eX]

[DOI]

IEEE Trans. Autom. Control., 2022

Learning neural state-space models: do we need a state estimator?

[BibT_eX]

[DOI]

CoRR, 2022

Direct identification of continuous-time LPV state-space models via an integral architecture.

[BibT_eX]

[DOI]

Autom., 2022

NARX Identification using Derivative-Based Regularized Neural Networks.

[BibT_eX]

[DOI]

L. H. Peeters

Gerben Izaak Beintema

Maarten Schoukens

Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Control Design via Bayesian Optimization with Safety Constraints.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Control Technology and Applications, 2022

2021

Continuous-time system identification with neural networks: Model structures and fitting criteria.

[BibT_eX]

[DOI]

Eur. J. Control, 2021

Deep learning with transfer functions: new applications in system identification.

[BibT_eX]

[DOI]

CoRR, 2021

2020

Integrated Neural Networks for Nonlinear Continuous-Time System Identification.

[BibT_eX]

[DOI]

Bojan Mavkov

IEEE Control. Syst. Lett., 2020

dynoNet: a neural network architecture for learning dynamical systems.

[BibT_eX]

[DOI]

CoRR, 2020

One-Stage Auto-Tuning Procedure of Robot Dynamics and Control Parameters for Trajectory Tracking Applications.

[BibT_eX]

[DOI]

Loris Roveda

Proceedings of the 17th International Conference on Ubiquitous Robots, 2020

2019

Performance-Oriented Model Learning for Data-Driven MPC Design.

[BibT_eX]

[DOI]

IEEE Control. Syst. Lett., 2019

Model structures and fitting criteria for system identification with neural networks.

[BibT_eX]

[DOI]

CoRR, 2019

Efficient Calibration of Embedded MPC.

[BibT_eX]

[DOI]

Alberto Bemporad

CoRR, 2019

2015

Least costly closed-loop performance diagnosis and plant re-identification.

[BibT_eX]

[DOI]

Int. J. Control, 2015

Data-driven model improvement for model-based control.

[BibT_eX]

[DOI]

Autom., 2015

Least-costly experiment design for uni-parametric linear models: An analytical approach.

[BibT_eX]

[DOI]

Max G. Potters

Proceedings of the 14th European Control Conference, 2015

2014

Experiment design for parameter estimation in nonlinear systems based on multilevel excitation.

[BibT_eX]

[DOI]

Håkan Hjalmarsson

Proceedings of the 13th European Control Conference, 2014

2013

Experiment design for batch-to-batch model-based learning control.

[BibT_eX]

[DOI]

Proceedings of the American Control Conference, 2013

2012

A unified experiment design framework for detection and identification in closed-loop performance diagnosis.

[BibT_eX]

[DOI]

Proceedings of the 51th IEEE Conference on Decision and Control, 2012

Batch-to-batch strategies for cooling crystallization.

[BibT_eX]

[DOI]

Ali Mesbah

Proceedings of the 51th IEEE Conference on Decision and Control, 2012

Iterative Learning Control of supersaturation in batch cooling crystallization.

[BibT_eX]

[DOI]

Ali Mesbah