10 papers
Directional Conformal Uncertainty Quantification from Learned Model Discrepancy
Cesare Donati, Fabrizio Dabbene, Martina Mammarella
We propose a conformal prediction framework for quantifying the error of physics-based predictors used in control, where simple models are preferred for synthesis, certification, a…
Identification of contractive Lur'e-type systems via kernel-based Lipschitz design
Cesare Donati, Fabrizio Dabbene, Constantino Lagoa +2
This paper addresses the problem of identifying contractive Lur'e-type systems. Specifically, it proposes an identification framework that integrates linear prior knowledge with a…
MPC-based motion planning for non-holonomic systems in non-convex domains
Matthias Lorenzen, Teodoro Alamo, Martina Mammarella +1
Motivated by the application of using model predictive control (MPC) for motion planning of autonomous mobile robots, a form of output tracking MPC for non-holonomic systems and wi…
A kernel-based approach to physics-informed nonlinear system identification
Cesare Donati, Martina Mammarella, Giuseppe C. Calafiore +3
This paper presents a kernel-based framework for physics-informed nonlinear system identification. The key contribution is a structured methodology that extends kernel-based techni…
Recursive feasibility for stochastic MPC and the rationale behind fixing flat tires
Mirko Fiacchini, Martina Mammarella, Fabrizio Dabbene
In this paper, we address the problem of designing stochastic model predictive control (SMPC) schemes for linear systems affected by unbounded disturbances. The contribution of the…
A Model-based Approach for Glucose Control via Physical Activity
Pierluigi Francesco De Paola, Alessandro Borri, Alessia Paglialonga +2
The role played by physical activity in slowing down the progression of type-2 diabetes is well recognized. However, except for general clinical guidelines, quantitative real-time…