Fabio Bonassi

Orcid: 0000-0002-7270-8185

According to our database1, Fabio Bonassi authored at least 17 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
Nonlinear MPC design for incrementally ISS systems with application to GRU networks.
Autom., January, 2024

On the equivalence of direct and indirect data-driven predictive control approaches.
CoRR, 2024

Internal Model Control design for systems learned by Control Affine Neural Nonlinear Autoregressive Exogenous Models.
CoRR, 2024

2023
Reconciling deep learning and control theory : recurrent neural networks for model-based control design
PhD thesis, 2023

Structured state-space models are deep Wiener models.
CoRR, 2023

Deep Long-Short Term Memory networks: Stability properties and Experimental validation.
Proceedings of the European Control Conference, 2023

2022
Recurrent Neural Network-based Internal Model Control design for stable nonlinear systems.
Eur. J. Control, 2022

Robust offset-free nonlinear model predictive control for systems learned by neural nonlinear autoregressive exogenous models.
CoRR, 2022

Towards lifelong learning of Recurrent Neural Networks for control design.
Proceedings of the European Control Conference, 2022

An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

2021
On the stability properties of Gated Recurrent Units neural networks.
Syst. Control. Lett., 2021

On Recurrent Neural Networks for learning-based control: recent results and ideas for future developments.
CoRR, 2021

Recurrent neural network-based Internal Model Control of unknown nonlinear stable systems.
CoRR, 2021

Nonlinear MPC for Offset-Free Tracking of systems learned by GRU Neural Networks.
CoRR, 2021

2020
Stability of discrete-time feed-forward neural networks in NARX configuration.
CoRR, 2020

LSTM Neural Networks: Input to State Stability and Probabilistic Safety Verification.
Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

Software-in-the-loop testing of a distributed optimal scheduling strategy for microgrids' aggregators.
Proceedings of the IEEE PES Innovative Smart Grid Technologies Europe, 2020


  Loading...