| Learning nonlinear state–space models using autoencoders D Masti, A Bemporad Automatica 129, 109666, 2021 | 108 | 2021 |
| Learning nonlinear state-space models using deep autoencoders D Masti, A Bemporad 2018 IEEE Conference on Decision and Control (CDC), 3862-3867, 2018 | 63 | 2018 |
| Learning binary warm starts for multiparametric mixed-integer quadratic programming D Masti, A Bemporad 2019 18th European Control Conference (ECC), 1494-1499, 2019 | 38 | 2019 |
| Railway cyber-security in the era of interconnected systems: a survey S Soderi, D Masti, YZ Lun IEEE Transactions on Intelligent Transportation Systems 24 (7), 6764-6779, 2023 | 28 | 2023 |
| Learning approximate semi-explicit hybrid MPC with an application to microgrids D Masti, T Pippia, A Bemporad, B De Schutter IFAC-PapersOnLine 53 (2), 5207-5212, 2020 | 20 | 2020 |
| Tuning LQR controllers: A sensitivity-based approach D Masti, M Zanon, A Bemporad IEEE Control Systems Letters 6, 932-937, 2021 | 13 | 2021 |
| A machine-learning approach to synthesize virtual sensors for parameter-varying systems D Masti, D Bernardini, A Bemporad European Journal of Control 61, 40-49, 2021 | 11 | 2021 |
| Learning affine predictors for MPC of nonlinear systems via artificial neural networks D Masti, F Smarra, A D’Innocenzo, A Bemporad IFAC-PapersOnLine 53 (2), 5233-5238, 2020 | 11 | 2020 |
| Cybersecurity considerations for communication based train control S Soderi, D Masti, M Hämäläinen, J Iinatti IEEE Access, 2023 | 8 | 2023 |
| NAW-NET: neural anti-windup control for saturated nonlinear systems V Breschi, D Masti, S Formentin, A Bemporad 2020 59th IEEE Conference on Decision and Control (CDC), 3335-3340, 2020 | 8 | 2020 |
| Direct data-driven design of neural reference governors D Masti, V Breschi, S Formentin, A Bemporad 2020 59th IEEE Conference on Decision and Control (CDC), 4955-4960, 2020 | 6 | 2020 |
| Learning virtual sensors for estimating the scheduling signal of parameter-varying systems D Masti, D Bernardini, A Bemporad 2019 27th Mediterranean Conference on Control and Automation (MED), 232-237, 2019 | 6 | 2019 |
| Development of an innovative and sustainable sail-drone A Benedetto, P Luca, M Marco, A Corrieri, D Masti, L Vanni 2017 IEEE International Conference on Environment and Electrical Engineering …, 2017 | 4 | 2017 |
| Counter-example guided inductive synthesis of control Lyapunov functions for uncertain systems D Masti, F Fabiani, G Gnecco, A Bemporad IEEE Control Systems Letters 7, 2047-2052, 2023 | 3 | 2023 |
| Auto-tuning of reference models in direct data-driven control D Masti, V Breschi, S Formentin, A Bemporad Automatica 155, 111110, 2023 | 1 | 2023 |
| AIPerf'24: 2nd International Workshop on Artificial Intelligence for Performance Modeling, Prediction, and Control E Incerto, M Litoiu, D Masti Companion of the 15th ACM/SPEC International Conference on Performance …, 2024 | | 2024 |
| A neural network-based approach to hybrid systems identification for control F Fabiani, B Stellato, D Masti, PJ Goulart arXiv preprint arXiv:2404.01814, 2024 | | 2024 |
| Let's Take Back Control: A Journey through a Novel Generation of Control Techniques for Performance Engineering D Masti Companion of the 2023 ACM/SPEC International Conference on Performance …, 2023 | | 2023 |
| Machine learning methods for control, identification, and estimation D Masti IMT School for Advanced Studies Lucca, 2021 | | 2021 |
Alberto BemporadProfessor of Control Systems, IMT Lucca, ItalyEmail verificata su imtlucca.it
