8 citations · 18 across the 11 of their papers we have counts for
11 papers
Learning a Contracting KKL-observer with Local Optimal Guarantees
Clara Lucía Galimberti, Johan Peralez, Daniele Astolfi +2
The Kazantzis-Kravaris-Luenberger (KKL) observer provides a general framework for nonlinear state estimation by immersing the system dynamics into a stable linear or nonlinear late…
PAC-Bayesian Optimal Control with Stability and Generalization Guarantees
Mahrokh Ghoddousi Boroujeni, Clara Lucía Galimberti, Andreas Krause +1
Stochastic Nonlinear Optimal Control (SNOC) seeks to minimize a cost function that accounts for random disturbances acting on a nonlinear dynamical system. Since the expectation ov…
Neural Identification of Feedback-Stabilized Nonlinear Systems
Mahrokh G. Boroujeni, Laura Meroi, Leonardo Massai +2
Neural networks have demonstrated remarkable success in modeling nonlinear dynamical systems. However, identifying these systems from closed-loop experimental data remains a challe…
Parametrizations of All Stable Closed-loop Responses: From Theory to Neural Network Control Design
Clara Lucía Galimberti, Luca Furieri, Giancarlo Ferrari-Trecate
The complexity of modern control systems necessitates architectures that achieve high performance while ensuring robust stability, particularly for nonlinear systems. In this work,…
Learning to Boost the Performance of Stable Nonlinear Systems
Luca Furieri, Clara Lucía Galimberti, Giancarlo Ferrari-Trecate
The growing scale and complexity of safety-critical control systems underscore the need to evolve current control architectures aiming for the unparalleled performances achievable…
A PAC-Bayesian Framework for Optimal Control with Stability Guarantees
Mahrokh Ghoddousi Boroujeni, Clara Lucía Galimberti, Andreas Krause +1
Stochastic Nonlinear Optimal Control (SNOC) involves minimizing a cost function that averages out the random uncertainties affecting the dynamics of nonlinear systems. For tractabi…
Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations
Daniele Martinelli, Clara Lucía Galimberti, Ian R. Manchester +2
In this work, we introduce and study a class of Deep Neural Networks (DNNs) in continuous-time. The proposed architecture stems from the combination of Neural Ordinary Differential…
Neural System Level Synthesis: Learning over All Stabilizing Policies for Nonlinear Systems
Luca Furieri, Clara Lucía Galimberti, Giancarlo Ferrari-Trecate
We address the problem of designing stabilizing control policies for nonlinear systems in discrete-time, while minimizing an arbitrary cost function. When the system is linear and…
Distributed neural network control with dependability guarantees: a compositional port-Hamiltonian approach
Luca Furieri, Clara Lucía Galimberti, Muhammad Zakwan +1
Large-scale cyber-physical systems require that control policies are distributed, that is, that they only rely on local real-time measurements and communication with neighboring ag…
Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by Design
Clara Lucía Galimberti, Luca Furieri, Liang Xu +1
Deep Neural Networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation. To address this problem, we propo…
A unified framework for Hamiltonian deep neural networks
Clara L. Galimberti, Liang Xu, Giancarlo Ferrari Trecate
Training deep neural networks (DNNs) can be difficult due to the occurrence of vanishing/exploding gradients during weight optimization. To avoid this problem, we propose a class o…