Software-in-the-loop testing of a distributed optimal scheduling strategy for microgrids' aggregators. - Fabio Bonassi; Alessio La Bella; Lorenzo Fagiano; Riccardo Scattolini; Donato Zarrilli; Pablo Almaleck (ISGT-Europe, 2020)

LSTM Neural Networks: Input to State Stability and Probabilistic Safety Verification. - Fabio Bonassi; Enrico Terzi; Marcello Farina; Riccardo Scattolini (L4DC, 2020)

Stability of discrete-time feed-forward neural networks in NARX configuration. - Fabio Bonassi; Marcello Farina; Riccardo Scattolini (CoRR, 2020)

Nonlinear MPC for Offset-Free Tracking of systems learned by GRU Neural Networks. - Fabio Bonassi; Caio Fabio Oliveira da Silva; Riccardo Scattolini (CoRR, 2021)

Recurrent neural network-based Internal Model Control of unknown nonlinear stable systems. - Fabio Bonassi; Riccardo Scattolini (CoRR, 2021)

On Recurrent Neural Networks for learning-based control: recent results and ideas for future developments. - Fabio Bonassi; Marcello Farina; Jing Xie 015; Riccardo Scattolini (CoRR, 2021)

On the stability properties of Gated Recurrent Units neural networks. - Fabio Bonassi; Marcello Farina; Riccardo Scattolini (Syst. Control. Lett., 2021)

An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models. - Fabio Bonassi; Jing Xie 015; Marcello Farina; Riccardo Scattolini (CDC, 2022)

Towards lifelong learning of Recurrent Neural Networks for control design. - Fabio Bonassi; Jing Xie 015; Marcello Farina; Riccardo Scattolini (ECC, 2022)

Robust offset-free nonlinear model predictive control for systems learned by neural nonlinear autoregressive exogenous models. - Jing Xie 015; Fabio Bonassi; Marcello Farina; Riccardo Scattolini (CoRR, 2022)

Recurrent Neural Network-based Internal Model Control design for stable nonlinear systems. - Fabio Bonassi; Riccardo Scattolini (Eur. J. Control, 2022)

Deep Long-Short Term Memory networks: Stability properties and Experimental validation. - Fabio Bonassi; Alessio La Bella; Giulio Panzani; Marcello Farina; Riccardo Scattolini (ECC, 2023)

Structured state-space models are deep Wiener models. - Fabio Bonassi; Carl R. Andersson; Per Mattsson; Thomas B. Schön (CoRR, 2023)

Reconciling deep learning and control theory : recurrent neural networks for model-based control design - Fabio Bonassi (2023)

Learning state observers for recurrent neural network models. - Fabio Bonassi; Carl R. Andersson; Per Mattsson; Thomas B. Schön (CDC, 2024)

Estimation and MPC control based on gated recurrent unit neural networks with unknown disturbances. - Eva Masero; Fabio Bonassi; Alessio La Bella; Riccardo Scattolini (CDC, 2024)

Internal Model Control design for systems learned by Control Affine Neural Nonlinear Autoregressive Exogenous Models. - Jing Xie 015; Fabio Bonassi; Riccardo Scattolini (CoRR, 2024)

Accounts of using the Tustin-Net architecture on a rotary inverted pendulum. - Stijn van Esch; Fabio Bonassi; Thomas B. Schön (CoRR, 2024)

On the Equivalence of Direct and Indirect Data-Driven Predictive Control Approaches. - Per Mattsson; Fabio Bonassi; Valentina Breschi; Thomas B. Schön (IEEE Control. Syst. Lett., 2024)

Nonlinear MPC design for incrementally ISS systems with application to GRU networks. - Fabio Bonassi; Alessio La Bella; Marcello Farina; Riccardo Scattolini (Autom., 2024)

Learning Control Affine Neural NARX Models for Internal Model Control Design. - Jing Xie 015; Fabio Bonassi; Riccardo Scattolini (IEEE Trans Autom. Sci. Eng., 2025)