23 citations · 41 across the 17 of their papers we have counts for
3 papers · 1 filter
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…