Angelo D. Bonzanini

Orcid: 0000-0003-4010-6099

According to our database1, Angelo D. Bonzanini authored at least 11 papers between 2019 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
Perception-aware model predictive control for constrained control in unknown environments.
Autom., February, 2024

2022
Learning-Based SMPC for Reference Tracking Under State-Dependent Uncertainty: An Application to Atmospheric Pressure Plasma Jets for Plasma Medicine.
IEEE Trans. Control. Syst. Technol., 2022

Performance-oriented model learning for control via multi-objective Bayesian optimization.
Comput. Chem. Eng., 2022

Scalable Estimation of Invariant Sets for Mixed-Integer Nonlinear Systems using Active Deep Learning.
Proceedings of the 61st IEEE Conference on Decision and Control, 2022

Multi-stage Perception-aware Chance-constrained MPC with Applications to Automated Driving.
Proceedings of the American Control Conference, 2022

2021
Fast approximate learning-based multistage nonlinear model predictive control using Gaussian processes and deep neural networks.
Comput. Chem. Eng., 2021

On the Stability Properties of Perception-aware Chance-constrained MPC in Uncertain Environments.
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

Perception-Aware Chance-Constrained Model Predictive Control for Uncertain Environments.
Proceedings of the 2021 American Control Conference, 2021

2020
Learning-based Stochastic Model Predictive Control with State-Dependent Uncertainty.
Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

Safe Learning-based Model Predictive Control under State- and Input-dependent Uncertainty using Scenario Trees.
Proceedings of the 59th IEEE Conference on Decision and Control, 2020

2019
A Constraint-Tightening Approach to Nonlinear Model Predictive Control with Chance Constraints for Stochastic Systems.
Proceedings of the 2019 American Control Conference, 2019


  Loading...