Zobrazeno 1 - 10
of 44
pro vyhledávání: '"Mammarella, Martina"'
This paper presents a tool for multi-step system identification that leverages first-order optimization and exact gradient computation. Drawing inspiration from neural network training and Automatic Differentiation (AD), the proposed method computes
Externí odkaz:
http://arxiv.org/abs/2410.03544
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 paper is twofold. First, motivated by the difficulty of guaranteeing r
Externí odkaz:
http://arxiv.org/abs/2406.13522
In this paper, we propose a unified framework for identifying interpretable nonlinear dynamical models that preserve physical properties. The proposed approach integrates physical principles with black-box basis functions to compensate for unmodeled
Externí odkaz:
http://arxiv.org/abs/2405.18186
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 directly into a backpropagation-based learning algorithm. The main result is
Externí odkaz:
http://arxiv.org/abs/2310.20567
Autor:
Mammarella, Martina, Donati, Cesare, Shimizu, Takumi, Suenaga, Masaya, Comba, Lorenzo, Biglia, Alessandro, Uto, Kuniaki, Hatanaka, Takeshi, Gay, Paolo, Dabbene, Fabrizio
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 3D map reconstruction. In this paper, we present an innovative approa
Externí odkaz:
http://arxiv.org/abs/2202.02758
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 sample-based approach, a probabilistic upper bound on the absolute value
Externí odkaz:
http://arxiv.org/abs/2105.14187
Autor:
Mammarella, Martina, Mirasierra, Victor, Lorenzen, Matthias, Alamo, Teodoro, Dabbene, Fabrizio
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 the approximating set, by defining families of simple-approximating s
Externí odkaz:
http://arxiv.org/abs/2101.06052
In recent years, the increasing interest in Stochastic model predictive control (SMPC) schemes has highlighted the limitation arising from their inherent computational demand, which has restricted their applicability to slow-dynamics and high-perform
Externí odkaz:
http://arxiv.org/abs/2005.10572
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 fundamental aspects like seasonal behaviour, regional mortality rates, and eff
Externí odkaz:
http://arxiv.org/abs/2004.06111
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 assumption on either independence nor Gaussianity. We revisit the rat
Externí odkaz:
http://arxiv.org/abs/2003.07241