3 citations · 6 across the 15 of their papers we have counts for
20 papers
Timescale Separation Through the Lens of Operator Theory
Guido Carnevale, Nicola Bastianello, Luca Schenato +2
Timescale separation is a powerful tool for analyzing interconnected dynamical systems. Meanwhile, operator theory provides a general framework for studying the convergence of iter…
Safe Learning Predictive Control for Ego-World Robotic Systems
Davide Valenti, Giuseppe Notarstefano
Safe autonomous navigation in shared environments requires the ability to anticipate and react to the latent behaviors of surrounding robots. In this paper, we propose SOWL-MPC, a…
Suboptimal and Reduced-Order MPC via Timescale Separation
Stefano Di Gregorio, Guido Carnevale, Giuseppe Notarstefano
In this paper, we propose a generalized framework for the design and analysis of suboptimal and reduced-order nonlinear Model Predictive Control (MPC) architectures. The proposed f…
On Reward-Balancing Methods for Reinforcement Learning
Simone Baroncini, Bahman Gharesifard, Giuseppe Notarstefano
This paper investigates the so-called reward-balancing methods, a novel class of algorithms for solving discounted-return reinforcement learning (RL) problems. These methods consis…
A Distributed Bilevel Framework for the Macroscopic Optimization of Multi-Agent Systems
Riccardo Brumali, Guido Carnevale, Sonia Martínez +1
In this paper, we propose a novel distributed algorithm to optimize the emergent macroscopic behavior of large-scale multi-agent systems via microscopic actions. We cast this task…
Model-Free Aggregative Cooperative Optimization via Randomized Gradient-Free Minimization and Exploration Momentum
Amir Mehrnoosh, Giuseppe Speciale, Riccardo Brumali +2
Aggregative cooperative optimization problems arise in distributed decision-making settings where each agent's objective depends on its own decision as well as on an aggregate vari…
Safe Control of Feedback-Interconnected Systems via Singular Perturbations
Stefano Di Gregorio, Guido Carnevale, Giuseppe Notarstefano
Control Barrier Functions (CBFs) have emerged as a powerful tool in the design of safety-critical controllers for nonlinear systems. In modern applications, complex systems often i…
Data-Driven Stabilization of Continuous-Time LTI Systems from Noisy Input-Output Data
Alessandro Bosso, Marco Borghesi, Andrea Iannelli +2
We present an approach to compute stabilizing controllers for continuous-time linear time-invariant systems directly from an input-output trajectory affected by process and measure…
Accelerated ADMM: Automated Parameter Tuning and Improved Linear Convergence
Meisam Tavakoli, Fabian Jakob, Guido Carnevale +2
This work studies the linear convergence of an accelerated scheme of the Alternating Direction Method of Multipliers (ADMM) for strongly convex and Lipschitz-smooth problems. We us…
Nonlinear MPC for Feedback-Interconnected Systems: a Suboptimal and Reduced-Order Model Approach
Stefano Di Gregorio, Guido Carnevale, Giuseppe Notarstefano
In this paper, we propose a suboptimal and reduced-order Model Predictive Control (MPC) architecture for discrete-time feedback-interconnected systems. The numerical MPC solver: (i…
DATA-DRIVEN PRONTO: a Model-free Solution for Numerical Optimal Control
Marco Borghesi, Lorenzo Sforni, Giuseppe Notarstefano
This article addresses the problem of data-driven numerical optimal control for unknown nonlinear systems. In our scenario, we suppose to have the possibility of performing multipl…
Data-Driven Control of Continuous-Time LTI Systems via Non-Minimal Realizations
Alessandro Bosso, Marco Borghesi, Andrea Iannelli +2
This article proposes an approach to design output-feedback controllers for unknown continuous-time linear time-invariant systems using only input-output data from a single experim…
Distributed equilibrium seeking in aggregative games: linear convergence under singular perturbations lens
Guido Carnevale, Filippo Fabiani, Filiberto Fele +2
We present a fully-distributed algorithm for Nash equilibrium seeking in aggregative games over networks. The proposed scheme endows each agent with a gradient-based scheme equippe…
Data-Driven Distributed Optimization via Aggregative Tracking and Deep-Learning
Riccardo Brumali, Guido Carnevale, Giuseppe Notarstefano
In this paper, we propose a novel distributed data-driven optimization scheme. In detail, we focus on the so-called aggregative framework, a scenario in which a set of agents aim t…
On Sufficient Richness for Linear Time-Invariant Systems
Marco Borghesi, Simone Baroncini, Guido Carnevale +2
Persistent excitation (PE) is a necessary and sufficient condition for uniform exponential parameter convergence in several adaptive, identification, and learning schemes. In this…
Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration
Guido Carnevale, Nicola Mimmo, Giuseppe Notarstefano
In this paper, we address Linear Quadratic Regulator (LQR) problems through a novel iterative algorithm named EXtremum-seeking Policy iteration LQR (EXP-LQR). The peculiarity of EX…
Multi-Robot Target Monitoring and Encirclement via Triggered Distributed Feedback Optimization
Lorenzo Pichierri, Guido Carnevale, Lorenzo Sforni +1
We design a distributed feedback optimization strategy, embedded into a modular ROS 2 control architecture, which allows a team of heterogeneous robots to cooperatively monitor and…
Towards Parameter-free Distributed Optimization: a Port-Hamiltonian Approach
Rodrigo Aldana-López, Alessandro Macchelli, Giuseppe Notarstefano +2
This paper introduces a novel distributed optimization technique for networked systems, which removes the dependency on specific parameter choices, notably the learning rate. Tradi…
Stability-Certified On-Policy Data-Driven LQR via Recursive Learning and Policy Gradient
Lorenzo Sforni, Guido Carnevale, Ivano Notarnicola +1
In this paper, we investigate a data-driven framework to solve Linear Quadratic Regulator (LQR) problems when the dynamics is unknown, with the additional challenge of providing st…
A Unifying System Theory Framework for Distributed Optimization and Games
Guido Carnevale, Nicola Mimmo, Giuseppe Notarstefano
This paper introduces a systematic methodological framework to design and analyze distributed algorithms for optimization and games over networks. Starting from a centralized metho…