8 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…
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…
Recovering nonlinear dynamics from non-uniform observations: A physics-based identification approach with practical case studies
Cesare Donati, Martina Mammarella, Fabrizio Dabbene +2
Uniform and smooth data collection is often infeasible in real-world scenarios. In this paper, we propose an identification framework to effectively handle the so-called non-unifor…
A scalable, gradient-stable approach to multi-step, nonlinear system identification using first-order methods
Cesare Donati, Martina Mammarella, Fabrizio Dabbene +2
This paper presents three main contributions to the field of multi-step system identification. First, drawing inspiration from Neural Network (NN) training, it introduces a tool fo…