23 citations · 41 across the 17 of their papers we have counts for
18 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…
Measured-state conditioned recursive feasibility for stochastic model predictive control
Mirko Fiacchini, Martina Mammarella, Fabrizio Dabbene
In this paper, we address the problem of designing stochastic model predictive control (MPC) schemes for linear systems affected by unbounded disturbances. The contribution of the…
Combining Off-White and Sparse Black Models in Multi-step Physics-based Systems Identification -- EXTENDED VERSION
Cesare Donati, Martina Mammarella, Fabrizio Dabbene +2
In this paper, we propose a unified framework for identifying interpretable nonlinear dynamical models that preserve physical properties. The proposed approach integrates physical…
One-shot backpropagation for multi-step prediction in physics-based system identification -- EXTENDED VERSION
Cesare Donati, Martina Mammarella, Fabrizio Dabbene +2
The aim of this paper is to present a novel physics-based framework for the identification of dynamical systems, in which the physical and structural insights are reflected directl…
3D Map Reconstruction of an Orchard using an Angle-Aware Covering Control Strategy
Martina Mammarella, Cesare Donati, Takumi Shimizu +7
In the last years, unmanned aerial vehicles are becoming a reality in the context of precision agriculture, mainly for monitoring, patrolling and remote sensing tasks, but also for…
Prediction error quantification through probabilistic scaling -- EXTENDED VERSION
Victor Mirasierra, Martina Mammarella, Fabrizio Dabbene +1
In this paper, we address the probabilistic error quantification of a general class of prediction methods. We consider a given prediction model and show how to obtain, through a sa…
Chance constrained sets approximation: A probabilistic scaling approach -- EXTENDED VERSION
Martina Mammarella, Victor Mirasierra, Matthias Lorenzen +2
In this paper, a sample-based procedure for obtaining simple and computable approximations of chance-constrained sets is proposed. The procedure allows to control the complexity of…
Computationally efficient stochastic MPC: a probabilistic scaling approach
Martina Mammarella, Teodoro Alamo, Fabrizio Dabbene +1
In recent years, the increasing interest in Stochastic model predictive control (SMPC) schemes has highlighted the limitation arising from their inherent computational demand, whic…
Open Data Resources for Fighting COVID-19
Teodoro Alamo, Daniel G. Reina, Martina Mammarella +1
We provide an insight into the open data resources pertinent to the study of the spread of Covid-19 pandemic and its control. We identify the variables required to analyze fundamen…
A probabilistic validation approach for penalty function design in Stochastic Model Predictive Control
Martina Mammarella, Teodoro Alamo, Sergio Lucia +1
In this paper, we consider a stochastic Model Predictive Control able to account for effects of additive stochastic disturbance with unbounded support, and requiring no restrictive…
Sample-based SMPC for tracking control of fixed-wing UAV: multi-scenario mapping
Martina Mammarella, Elisa Capello, Fabrizio Dabbene
In this paper, a guidance and tracking control strategy for fixed-wing Unmanned Aerial Vehicle (UAV) autopilots is presented. The proposed control exploits recent results on sample…
A Tube-based Robust MPC for a Fixed-wing UAV: an Application for Precision Farming
Martina Mammarella, Elisa Capello
The techniques of precision agriculture include the possibility to execute crop monitoring tasks through the application of Unmanned Aerial Vehicles (UAVs). These platforms are fle…
An Offline-Sampling SMPC Framework with Application to Automated Space Maneuvers
Martina Mammarella, Matthias Lorenzen, Elisa Capello +5
In this paper, a sampling-based Stochastic Model Predictive Control algorithm is proposed for discrete-time linear systems subject to both parametric uncertainties and additive dis…