activity
20122024
most citedOn Recurrent Neural Networks for learning-based control: recent results and ideas for future developments

95 citations · 364 across the 28 of their papers we have counts for

collaborators

33 papers

eess.SY2024★ 1 cited

Moving horizon partition-based state estimation of large-scale systems -- Revised version

Marcello Farina, Giancarlo Ferrari-Trecate, Riccardo Scattolini

This report presents three Moving Horizon Estimation (MHE) methods for discrete-time partitioned linear systems, i.e. systems decomposed into coupled subsystems with non-overlappin…

eess.SY2023★ 24 cited

Nonlinear MPC design for incrementally ISS systems with application to GRU networks

Fabio Bonassi, Alessio La Bella, Marcello Farina +1

This brief addresses the design of a Nonlinear Model Predictive Control (NMPC) strategy for exponentially incremental Input-to-State Stable (ISS) systems. In particular, a novel fo…

math.OC2023★ 7 cited

An optimal hierarchical control scheme for smart generation units: an application to combined steam and electricity generation

Stefano Spinelli, Marcello Farina, Andrea Ballarino

Optimal management of thermal and energy grids with fluctuating demand and prices requires to orchestrate the generation units (GU) among all their operating modes. A hierarchical…

eess.SY2023★ 4 cited

A Hierarchical Architecture for Optimal Unit Commitment and Control of an Ensemble of Steam Generators

Stefano Spinelli, Marcello Farina, Andrea Ballarino

A hierarchical architecture for the optimal management of an ensemble of steam generators is presented. The subsystems are coordinated by a multilayer scheme for jointly sustaining…

eess.SY2023★ 1 cited

Deep Long-Short Term Memory networks: Stability properties and Experimental validation

Fabio Bonassi, Alessio La Bella, Giulio Panzani +2

The aim of this work is to investigate the use of Incrementally Input-to-State Stable (ISS) deep Long Short Term Memory networks (LSTMs) for the identification of nonlinear dyna…

eess.SY2022★ 2 cited

An incremental input-to-state stability condition for a generic class of recurrent neural networks

William D'Amico, Alessio La Bella, Marcello Farina

This paper proposes a novel sufficient condition for the incremental input-to-state stability of a generic class of recurrent neural networks (RNNs). The established condition is c…