Publications by Luca Furieri | EPFL Graph Search

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Toward bridging classical optimal control and online learning, regret minimization has recently been proposed as a control design criterion. This competitive paradigm penalizes the loss relative to the optimal control actions chosen by a clairvoyant policy ...

The complexity of modern control systems necessitates architectures that achieve high performance while ensuring robust stability, particularly for nonlinear systems. In this work, we tackle the challenge of designing output-feedback controllers to boost t ...

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 the cost is convex, the System Level Synthesis (SLS) approach offers an effe ...

It is known that the set of internally stabilizing controller Cstab is non-convex, but it admits convex characterizations using certain closed-loop maps: a classical result is the Youla parameterization, and two recent notions are the system-level paramete ...

As we transition toward the deployment of data-driven controllers for black-box cyberphysical systems, complying with hard safety constraints becomes a primary concern. Two key aspects should be addressed when input-output data are corrupted by noise: how ...

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 agents. Optimal Distributed Control (ODC) problems are, however, highly intrac ...

In this work, we focus on the design of optimal controllers that must comply with an information structure. State-of-the-art approaches do so based on the H2 or H∞ norm to minimize the expected or worst-case cost in the presence of stochastic or adversaria ...

This paper proposes a novel approach to improve the performance of distributed nonlinear control systems while preserving stability by leveraging Deep Neural Networks (DNNs). We build upon the Neural System Level Synthesis (Neur-SLS) framework and introduc ...

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 Equations (Neural ODEs) with the model structure of recently introduced Rec ...

This paper characterizes a new parametrization of nonlinear networked incrementally L2 -bounded operators in discrete time. The distinctive novelty is that our parametrization is free - that is, a sparse large-scale operator with bounded incremental L2 gai ...

We consider control of dynamical systems through the lens of competitive analysis. Most prior work in this area focuses on minimizing regret, that is, the loss relative to an ideal clairvoyant policy that has noncausal access to past, present, and future d ...

Recent work in data-driven control has revived behavioral theory to perform a variety of complex control tasks, by directly plugging libraries of past input-output trajectories into optimal control problems. Despite recent advances, a key aspect remains un ...

The paper aims at minimizing DC voltage oscillations in offshore multiterminal high-voltage direct current (HVDC) grids based on modular multilevel converters (MMCs). The DC voltage stability is a crucial factor in multiterminal HVDC networks since it is a ...

Deep Neural Networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation. To address this problem, we propose a general class of Hamiltonian DNNs (H-DNNs) that stem from the discretiz ...

The increasing reliance on numerical methods for controlling dynamical systems and training machine learning models underscores the need to devise algorithms that dependably and efficiently navigate complex optimization landscapes. Classical gradient desce ...

We consider control of uncertain linear time-varying stochastic systems from the perspective of regret minimization. Specifically, we focus on the problem of designing a feedback controller that minimizes the loss relative to a clairvoyant optimal policy t ...

How can we synthesize a safe and near-optimal control policy for a partially-observed system, if all we are given is one historical input/output trajectory that has been corrupted by noise? To address this challenge, we suggest a novel data-driven controll ...

Luca Furieri

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On the Guarantees of Minimizing Regret in Receding Horizon

Parametrizations of all stable closed-loop responses: From theory to neural network control design

Neural System Level Synthesis: Learning over All Stabilizing Policies for Nonlinear Systems

System-level, input-output and new parameterizations of stabilizing controllers, and their numerical computation

Near-Optimal Design of Safe Output-Feedback Controllers From Noisy Data

Distributed neural network control with dependability guarantees: a compositional port-Hamiltonian approach

Closing the Gap to Quadratic Invariance: A Regret Minimization Approach to Optimal Distributed Control

Optimal distributed control with stability guarantees by training a network of neural closed-loop maps

Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations

Unconstrained Learning of Networked Nonlinear Systems via Free Parametrization of Stable Interconnected Operators

Follow the Clairvoyant: an Imitation Learning Approach to Optimal Control

A Behavioral Input-Output Parametrization of Control Policies with Suboptimality Guarantees

Optimal Linear Controller for Minimizing DC Voltage Oscillations in MMC-Based Offshore Multiterminal HVDC Grids

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

Learning to Optimize with Convergence Guarantees Using Nonlinear System Theory

Regret optimal control for uncertain stochastic systems

Learning Robustly Safe Output Feedback Controllers from Noisy Data with Performance Guarantees

Erratum to “Learning to Boost the Performance of Stable Nonlinear Systems”