Fabio Bonassi

PhD Candidate

Dipartimento di Elettronica, Infomazione e Bioingegneria, Politecnico di Milano

Fabio Bonassi received his B.Sc. and M.Sc. in Automation & Control Engineering in 2016 and 2018, respectively, from Politecnico di Milano, Italy. In 2019, he received the “Claudio Maffezzoni Prize” for the best master thesis for the “application of advanced automation and control techniques in highly technological contexts”. Since November 2019, he is pursuing a PhD at  Politecnico di Milano, working under the supervision of prof. Riccardo Scattolini.

Project description

The research theme under investigation concerns the use of Neural Networks for data-driven control of unknown dynamical systems. Neural Networks have proven to be powerful tools for nonlinear system identification and control design, yet little results advocating their use in theoretically-sound contexts are available in the literature.

In this regard, research work has been devoted on the Input-to-State Stability (ISS) and Incremental Input-to-State Stability (𝛿ISS) properties of Recurrent Neural Networks (RNNs). In particular, sufficient conditions have been devised under which the RNN architectures used for identification (such as NARXs, LSTMs and GRUs) are guaranteed to enjoy these properties.

The ISS and 𝛿ISS are useful stability notions for nonlinear systems, which have been leveraged to perform, among other things, the safety verification of the model and to design Model Predictive Control regulators with guaranteed nominal closed-loop stability and recursive feasibility.


Xie, Jing; Bonassi, Fabio; Scattolini, Riccardo

Internal Model Control design for systems learned by Control Affine Neural Nonlinear Autoregressive Exogenous Models Working paper

2024, (Submitted to IEEE Transactions on Automation Science and Engineering).

Abstract | Links | BibTeX


Bonassi, Fabio; Bella, Alessio La; Farina, Marcello; Scattolini, Riccardo

Nonlinear MPC design for incrementally ISS systems with application to GRU networks Journal Article

In: Automatica, vol. 159, iss. 11381, pp. 111381, 2024.

Links | BibTeX


Bonassi, Fabio; Bella, Alessio La; Panzani, Giulio; Farina, Marcello; Scattolini, Riccardo

Deep Long-Short Term Memory networks: Stability properties and Experimental validation Proceedings Article

In: 2023 European Control Conference (ECC), pp. 1-6, IEEE, Bucharest, Romania, 2023, ISBN: 978-3-907144-08-4.

Abstract | Links | BibTeX


Xie, Jing; Bonassi, Fabio; Farina, Marcello; Scattolini, Riccardo

Robust offset-free nonlinear model predictive control for systems learned by neural nonlinear autoregressive exogenous models Journal Article

In: International Journal of Robust and Nonlinear Control, 2023.

Abstract | Links | BibTeX


Bonassi, Fabio

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


Abstract | Links | BibTeX


Bonassi, Fabio; Farina, Marcello; Xie, Jing; Scattolini, Riccardo

An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models Proceedings Article

In: 2022 IEEE 61st Conference on Decision and Control (CDC), pp. 2123-2128, IEEE, 2022, ISBN: 978-1-6654-6761-2.

Abstract | Links | BibTeX


Bonassi, Fabio; Farina, Marcello; Xie, Jing; Scattolini, Riccardo

On Recurrent Neural Networks for learning-based control: recent results and ideas for future developments Journal Article

In: Journal of Process Control, vol. 114, pp. 92-104, 2022, ISSN: 0959-1524.

Abstract | Links | BibTeX


Bonassi, Fabio; Xie, Jing; Farina, Marcello; Scattolini, Riccardo

Towards lifelong learning of Recurrent Neural Networks for control design Proceedings Article

In: 2022 European Control Conference (ECC), pp. 2018–2023, IEEE 2022.



Bonassi, Fabio; Scattolini, Riccardo

Recurrent Neural Network-based Internal Model Control Design for Stable Nonlinear Systems Journal Article

In: European Journal of Control, vol. 65, pp. 100632, 2022, ISSN: 0947-3580.

Abstract | Links | BibTeX