Michelangelo Bin

Math. Program., November, 2024

A comparison of centrality measures and their role in controlling the spread in epidemic networks.

[BibT_eX]

[DOI]

Int. J. Control, 2024

Kernel-Based Learning of Stable Nonlinear Systems.

[BibT_eX]

[DOI]

Matteo Scandella

CoRR, 2024

Canceling output disturbances in observer design through internal model filters.

[BibT_eX]

[DOI]

Autom., 2024

Robust internal models with a star-shaped attractor are linear.

[BibT_eX]

[DOI]

Daniele Astolfi

Autom., 2024

2023

The Gradient Tracking Is a Distributed Integral Action.

[BibT_eX]

[DOI]

Giuseppe Notarstefano

IEEE Trans. Autom. Control., December, 2023

Robust and scalable distributed recursive least squares.

[BibT_eX]

[DOI]

Ilario Antonio Azzollini

Autom., December, 2023

A Small-Gain Theory for Abstract Systems on Topological Spaces.

[BibT_eX]

[DOI]

IEEE Trans. Autom. Control., August, 2023

About Robustness of Control Systems Embedding an Internal Model.

[BibT_eX]

[DOI]

Daniele Astolfi

IEEE Trans. Autom. Control., March, 2023

Discrete-Time Distributed Optimization for Linear Uncertain Multi-Agent Systems.

[BibT_eX]

[DOI]

Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

An Internal Model Principle for Open Systems.

[BibT_eX]

[DOI]

Rodolphe Sepulchre

Proceedings of the 62nd IEEE Conference on Decision and Control, 2023

2022

Generalized recursive least squares: Stability, robustness, and excitation.

[BibT_eX]

[DOI]

Syst. Control. Lett., 2022

Data-driven Output Regulation via Gaussian Processes and Luenberger Internal Models.

[BibT_eX]

[DOI]

Lorenzo Gentilini

CoRR, 2022

Adaptive Nonlinear Regulation via Gaussian Process.

[BibT_eX]

[DOI]

Lorenzo Gentilini

CoRR, 2022

Internal Models in Control, Bioengineering, and Neuroscience.

[BibT_eX]

[DOI]

Annu. Rev. Control. Robotics Auton. Syst., 2022

Group-Based Dimensionality Reduction and Estimation for Heterogeneous Large-Scale Traffic Networks.

[BibT_eX]

[DOI]

Matteo Scandella

Proceedings of the European Control Conference, 2022

Adaptive Nonlinear Regulation via Gaussian Processes.

[BibT_eX]

[DOI]

Lorenzo Gentilini

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

Stability, Linear Convergence, and Robustness of the Wang-Elia Algorithm for Distributed Consensus Optimization.

[BibT_eX]

[DOI]

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

2021

Model Identification and Adaptive State Observation for a Class of Nonlinear Systems.

[BibT_eX]

[DOI]

IEEE Trans. Autom. Control., 2021

Approximate Nonlinear Regulation via Identification-Based Adaptive Internal Models.

[BibT_eX]

[DOI]

Pauline Bernard

IEEE Trans. Autom. Control., 2021

Post-lockdown abatement of COVID-19 by fast periodic switching.

[BibT_eX]

[DOI]

PLoS Comput. Biol., 2021

Output Regulation by Postprocessing Internal Models for a Class of Multivariable Nonlinear Systems.

[BibT_eX]

[DOI]

CoRR, 2021

On node ranking in graphs.

[BibT_eX]

[DOI]

CoRR, 2021

A distributed methodology for approximate uniform global minimum sharing.

[BibT_eX]

[DOI]

Autom., 2021

Hysteresis-based supervisory control with application to non-pharmaceutical containment of COVID-19.

[BibT_eX]

[DOI]

Emanuele Crisostomi

Pietro Ferraro

Robert Shorten

Sebastian Stein

Annu. Rev. Control., 2021

Robust Frequency Estimation of Multi-Harmonic Signals.

[BibT_eX]

[DOI]

Ilario Antonio Azzollini

Pauline Bernard

Proceedings of the 2021 European Control Conference, 2021

Traffic-Light Control at Urban Intersections Using Expected Waiting-Time Information.

[BibT_eX]

[DOI]

Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

2020

"Class-Type" Identification-Based Internal Models in Multivariable Nonlinear Output Regulation.

[BibT_eX]

[DOI]

IEEE Trans. Autom. Control., 2020

On Fast Multi-Shot Epidemic Interventions for Post Lock-Down Mitigation: Implications for Simple Covid-19 Models.

[BibT_eX]

[DOI]

CoRR, 2020

Stable Neural Flows.

[BibT_eX]

[DOI]

CoRR, 2020

Adaptive output regulation via nonlinear Luenberger observer-based internal models and continuous-time identifiers.

[BibT_eX]

[DOI]

Pauline Bernard

Autom., 2020

2019

Adaptive output regulation for linear systems via discrete-time identifiers.

[BibT_eX]

[DOI]

Andrew R. Teel

Autom., 2019

Adaptive Output Regulation for Multivariable Linear Systems via Slow Identifiers.

[BibT_eX]

[DOI]

Alessandro Melis

Proceedings of the 17th European Control Conference, 2019

A System Theoretical Perspective to Gradient-Tracking Algorithms for Distributed Quadratic Optimization.

[BibT_eX]

[DOI]

Giuseppe Notarstefano

Proceedings of the 58th IEEE Conference on Decision and Control, 2019

2018

Stability Analysis of Nonlinear Repetitive Control Schemes.

[BibT_eX]

[DOI]

IEEE Control. Syst. Lett., 2018

Adaptive Post-Processing Internal Models Design for MIMO Minimum-Phase Nonlinear Systems.

[BibT_eX]

[DOI]

CoRR, 2018

Results on Adaptive Output Regulation for Linear Systems by Least-Squares Identifiers.

[BibT_eX]

[DOI]

Andrew R. Teel

Proceedings of the 57th IEEE Conference on Decision and Control, 2018

The Chicken-Egg Dilemma and the Robustness Issue in Nonlinear Output Regulation with a Look Towards Adaptation and Universal Approximators.

[BibT_eX]

[DOI]

Proceedings of the 57th IEEE Conference on Decision and Control, 2018

About Robustness of Internal Model-Based Control for Linear and Nonlinear Systems.

[BibT_eX]

[DOI]

Proceedings of the 57th IEEE Conference on Decision and Control, 2018

2017

Boundary Estimation of Boundary Parameters for Linear Hyperbolic PDEs.

[BibT_eX]

[DOI]

Florent Di Meglio

IEEE Trans. Autom. Control., 2017

Adaptive output regulation via post-processing internal models and hybrid identifiers.

[BibT_eX]

[DOI]

Proceedings of the 56th IEEE Annual Conference on Decision and Control, 2017

Robust design of internal models by discrete recursive least squares identifiers.

[BibT_eX]

[DOI]

Andrew R. Teel

Proceedings of the 2017 American Control Conference, 2017

2016

Robust internal model design by nonlinear regression via low-power high-gain observers.

[BibT_eX]

[DOI]

Daniele Astolfi