4 citations · 8 across the 5 of their papers we have counts for
5 papers
Robust tracking MPC for perturbed nonlinear systems -- Extended version
Marco Polver, Daniel Limon, Fabio Previdi +1
This paper presents a novel robust predictive controller for constrained nonlinear systems that is able to track piece-wise constant setpoint signals. The tracking model predictive…
Integrating Biological-Informed Recurrent Neural Networks for Glucose-Insulin Dynamics Modeling
Stefano De Carli, Nicola Licini, Davide Previtali +2
Type 1 Diabetes (T1D) management is a complex task due to many variability factors. Artificial Pancreas (AP) systems have alleviated patient burden by automating insulin delivery t…
Infinity-norm-based Input-to-State-Stable Long Short-Term Memory networks: a thermal systems perspective
Stefano De Carli, Davide Previtali, Leandro Pitturelli +3
Recurrent Neural Networks (RNNs) have shown remarkable performances in system identification, particularly in nonlinear dynamical systems such as thermal processes. However, stabil…
Robust contraction-based model predictive control for nonlinear systems
Marco Polver, Daniel Limon, Fabio Previdi +1
Model Predictive Control (MPC) is a widely known control method that has proved to be particularly effective in multivariable and constrained control. Closed-loop stability and rec…
CHoKI-based MPC for blood glucose regulation in Artificial Pancreas
Beatrice Sonzogni, José María Manzano, Marco Polver +2
This work presents a Model Predictive Control (MPC) for the artificial pancreas, which is able to autonomously manage basal insulin injections in type 1 diabetic patients. Specific…