Marco Forgione

CoRR, 2024

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

[BibT_eX]

[DOI]

CoRR, 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

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