48 citations · 294 across the 36 of their papers we have counts for
36 papers
Data-Driven Optimal Distributed Controller Synthesis via Spatial Regret
Vaibhav Gupta, Daniele Martinelli, Giancarlo Ferrari-Trecate +2
In this paper, we present a novel method for synthesising an optimal distributed spatial regret controller using experimentally obtained frequency-response data. Spatial regret pro…
Learning Over-Relaxation Policies for ADMM with Convergence Guarantees
Junan Lin, Paul J. Goulart, Luca Furieri
The Alternating Direction Method of Multipliers (ADMM) is a widely used method for structured convex optimization, and its practical performance depends strongly on the choice of p…
Stability-Preserving Online Adaptation of Neural Closed-loop Maps
Danilo Saccani, Luca Furieri, Giancarlo Ferrari-Trecate
The growing complexity of modern control tasks calls for controllers that can react online as objectives and disturbances change, while preserving closed-loop stability. Recent app…
Safety-Aware Performance Boosting for Constrained Nonlinear Systems
Danilo Saccani, Haoming Shen, Luca Furieri +1
We study a control architecture for nonlinear constrained systems that integrates a performance-boosting (PB) controller with a scheduled Predictive Safety Filter (PSF). The PSF ac…
Characterizing all locally exponentially stabilizing controllers as a linear feedback plus learnable nonlinear Youla dynamics
Luca Furieri
We derive a state-space characterization of all dynamic state-feedback controllers that make an equilibrium of a nonlinear input-affine continuous-time system locally exponentially…
Distributed Control of Network Systems in the Space of Stabilizing Graph Neural Network Policies
John Cao, Luca Furieri
We study distributed control of networked systems through reinforcement learning, where neural policies must be simultaneously scalable, expressive and stabilizing. We introduce a…
A graph-informed regret metric for optimal distributed control
Daniele Martinelli, Andrea Martin, Giancarlo Ferrari-Trecate +1
We consider the optimal control of large-scale systems using distributed controllers whose network topology mirrors the coupling graph between subsystems. In this work, we introduc…
Learning to optimize with guarantees: a complete characterization of linearly convergent algorithms
Andrea Martin, Ian R. Manchester, Luca Furieri
The design of many classical optimization algorithms is driven by the certification of linear convergence rates over classes of optimization problems. In this paper, we consider th…
MAD: A Magnitude And Direction Policy Parametrization for Stability Constrained Reinforcement Learning
Luca Furieri, Sucheth Shenoy, Danilo Saccani +2
We introduce magnitude and direction (MAD) policies, a policy parameterization for reinforcement learning (RL) that preserves Lp closed-loop stability for nonlinear dynamical syste…
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…
Optimal distributed control with stability guarantees by training a network of neural closed-loop maps
Danilo Saccani, Leonardo Massai, Luca Furieri +1
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 bu…
Learning to optimize with convergence guarantees using nonlinear system theory
Andrea Martin, Luca Furieri
The increasing reliance on numerical methods for controlling dynamical systems and training machine learning models underscores the need to devise algorithms that dependably and ef…
Unconstrained learning of networked nonlinear systems via free parametrization of stable interconnected operators
Leonardo Massai, Danilo Saccani, Luca Furieri +1
This paper characterizes a new parametrization of nonlinear networked incrementally -bounded operators in discrete time. The distinctive novelty is that our parametrization is…
Closing the Gap to Quadratic Invariance: a Regret Minimization Approach to Optimal Distributed Control
Daniele Martinelli, Andrea Martin, Giancarlo Ferrari-Trecate +1
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 Hinfty norm to mini…
On the Guarantees of Minimizing Regret in Receding Horizon
Andrea Martin, Luca Furieri, Florian Dörfler +2
Towards bridging classical optimal control and online learning, regret minimization has recently been proposed as a control design criterion. This competitive paradigm penalizes th…
Regret Optimal Control for Uncertain Stochastic Systems
Andrea Martin, Luca Furieri, Florian Dörfler +2
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 c…
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…
Follow the Clairvoyant: an Imitation Learning Approach to Optimal Control
Andrea Martin, Luca Furieri, Florian Dörfler +2
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…
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…
Safe Control with Minimal Regret
Andrea Martin, Luca Furieri, Florian Dörfler +2
As we move towards safety-critical cyber-physical systems that operate in non-stationary and uncertain environments, it becomes crucial to close the gap between classical optimal c…
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…
Near-Optimal Design of Safe Output Feedback Controllers from Noisy Data
Luca Furieri, Baiwei Guo, Andrea Martin +1
As we transition towards the deployment of data-driven controllers for black-box cyberphysical systems, complying with hard safety constraints becomes a primary concern. Two key as…