4 citations · 8 across the 8 of their papers we have counts for
8 papers
Chaos-Free Networks are Stable Recurrent Neural Networks
Stefano De Carli, Davide Previtali, Mirko Mazzoleni +1
Gated Recurrent Neural Networks (RNNs) are widely used for nonlinear system identification due to their high accuracy, although they often exhibit complex, chaotic dynamics that ar…
Stability properties of Minimal Gated Unit neural networks
Stefano De Carli, Davide Previtali, Mirko Mazzoleni +1
In this work, we address the need for efficient and formally stable Recurrent Neural Networks (RNNs) in environments with limited computational resources by analyzing the stability…
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…
A virtual sensor fusion approach for state of charge estimation of lithium-ion cells
Davide Previtali, Daniele Masti, Mirko Mazzoleni +1
This paper addresses the estimation of the State Of Charge (SOC) of lithium-ion cells via the combination of two widely used paradigms: Kalman Filters (KFs) equipped with Equivalen…
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…