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2020 – today
- 2024
[c7]Emilio Incerto
, Marin Litoiu, Daniele Masti:
AIPerf'24: 2nd International Workshop on Artificial Intelligence for Performance Modeling, Prediction, and Control. ICPE (Companion) 2024: 93- [i4]Filippo Fabiani, Bartolomeo Stellato, Daniele Masti, Paul J. Goulart:
A neural network-based approach to hybrid systems identification for control. CoRR abs/2404.01814 (2024) - 2023
- [j7]Simone Soderi, Daniele Masti, Matti Hämäläinen, Jari H. Iinatti:
Cybersecurity Considerations for Communication Based Train Control. IEEE Access 11: 92312-92321 (2023) - [j6]Daniele Masti, Valentina Breschi, Simone Formentin, Alberto Bemporad:
Auto-tuning of reference models in direct data-driven control. Autom. 155: 111110 (2023) - [j5]Daniele Masti, Filippo Fabiani, Giorgio Gnecco, Alberto Bemporad:
Counter-Example Guided Inductive Synthesis of Control Lyapunov Functions for Uncertain Systems. IEEE Control. Syst. Lett. 7: 2047-2052 (2023) - [j4]Simone Soderi, Daniele Masti, Yuriy Zacchia Lun:
Railway Cyber-Security in the Era of Interconnected Systems: A Survey. IEEE Trans. Intell. Transp. Syst. 24(7): 6764-6779 (2023) - [c6]Daniele Masti:
Let's Take Back Control: A Journey through a Novel Generation of Control Techniques for Performance Engineering. ICPE (Companion) 2023: 135 - [i3]Daniele Masti, Filippo Fabiani, Giorgio Gnecco, Alberto Bemporad:
Counter-example guided inductive synthesis of control Lyapunov functions for uncertain systems. CoRR abs/2303.10024 (2023) - 2022
- [j3]Daniele Masti, Mario Zanon, Alberto Bemporad:
Tuning LQR Controllers: A Sensitivity-Based Approach. IEEE Control. Syst. Lett. 6: 932-937 (2022) - [i2]Simone Soderi, Daniele Masti, Yuriy Zacchia Lun:
Railway cyber-security in the era of interconnected systems: a survey. CoRR abs/2207.13412 (2022) - 2021
- [j2]Daniele Masti, Alberto Bemporad:
Learning nonlinear state-space models using autoencoders. Autom. 129: 109666 (2021) - [j1]Daniele Masti, Daniele Bernardini, Alberto Bemporad:
A machine-learning approach to synthesize virtual sensors for parameter-varying systems. Eur. J. Control 61: 40-49 (2021) - [i1]Daniele Masti, Daniele Bernardini, Alberto Bemporad:
A machine-learning approach to synthesize virtual sensors for parameter-varying systems. CoRR abs/2103.12324 (2021) - 2020
- [c5]Valentina Breschi, Daniele Masti, Simone Formentin, Alberto Bemporad:
NAW-NET: neural anti-windup control for saturated nonlinear systems. CDC 2020: 3335-3340 - [c4]Daniele Masti, Valentina Breschi, Simone Formentin, Alberto Bemporad:
Direct data-driven design of neural reference governors. CDC 2020: 4955-4960
2010 – 2019
- 2019
- [c3]Daniele Masti, Alberto Bemporad:
Learning binary warm starts for multiparametric mixed-integer quadratic programming. ECC 2019: 1494-1499 - [c2]Daniele Masti, Daniele Bernardini, Alberto Bemporad:
Learning virtual sensors for estimating the scheduling signal of parameter-varying systems. MED 2019: 232-237 - 2018
- [c1]Daniele Masti, Alberto Bemporad:
Learning Nonlinear State-Space Models Using Deep Autoencoders. CDC 2018: 3862-3867
Coauthor Index
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