257 citations · 996 across the 51 of their papers we have counts for
56 papers
Failure-Mechanism Transferability of Cumulative-Damage Features for Health State Estimation of SiC Power Modules
Mattia Scarpa, Evgeny Kusmenko, Francesco Toso +3
Data-driven health-state estimators for SiC (Silica-Carbide) power modules typically report their performance on a single accelerated-aging campaign, and how that performance trans…
Physics-Informed Condition Monitoring of SiC Power Modules
Mattia Scarpa, Evgeny Kusmenko, Francesco Toso +3
Silicon carbide (SiC) power modules are increasingly deployed in automotive traction inverters, where condition monitoring is essential to prevent in-service failures. Despite exte…
Adaptive Stepsizes With Certified Convergence in Distributed Gradient Tracking With Quadratic Costs
Yifan Wang, Luca Ballotta, Ruggero Carli +3
In this work, we propose an adaptive stepsize rule with guaranteed convergence for Distributed Gradient Tracking applied to scalar quadratic problems with heterogeneous curvatures.…
Pursuing Optimal Stepsize in Adaptive Gradient-Based Quadratic Optimization
Yifan Wang, Luca Ballotta, Ruggero Carli +2
In this paper, we address the problem of achieving fast convergence in gradient descent for quadratic functions without relying on a priori knowledge of global function parameters.…
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…
Data-driven Power Loss Identification through Physics-Based Thermal Model Backpropagation
Mattia Scarpa, Francesco Pase, Ruggero Carli +2
Digital twins for power electronics require accurate power losses whose direct measurements are often impractical or impossible in real-world applications. This paper presents a no…
The Bayesian Separation Principle for Data-driven Control
Giacomo Baggio, Ruggero Carli, Riccardo Alessandro Grimaldi +1
In this paper we investigate the existence of a separation principle between model identification and control design in the context of model predictive control. First, we clarify t…
Multi-Agent Optimization and Learning: A Non-Expansive Operators Perspective
Nicola Bastianello, Luca Schenato, Ruggero Carli
Multi-agent systems are increasingly widespread in a range of application domains, with optimization and learning underpinning many of the tasks that arise in this context. Differe…
Joint torques prediction of a robotic arm using neural networks
Giulia d'Addato, Ruggero Carli, Eurico Pedrosa +3
Accurate dynamic models are crucial for many robotic applications. Traditional approaches to deriving these models are based on the application of Lagrangian or Newtonian mechanics…
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification
Giulio Giacomuzzos, Ruggero Carli, Diego Romeres +1
Learning the inverse dynamics of robots directly from data, adopting a black-box approach, is interesting for several real-world scenarios where limited knowledge about the system…
A Control Theoretical Approach to Online Constrained Optimization
Umberto Casti, Nicola Bastianello, Ruggero Carli +1
In this paper we focus on the solution of online problems with time-varying, linear equality and inequality constraints. Our approach is to design a novel online algorithm by lever…
ADMM-Tracking Gradient for Distributed Optimization over Asynchronous and Unreliable Networks
Guido Carnevale, Nicola Bastianello, Giuseppe Notarstefano +1
In this paper, we propose a novel distributed algorithm for consensus optimization over networks and a robust extension tailored to deal with asynchronous agents and packet losses.…
Forward Dynamics Estimation from Data-Driven Inverse Dynamics Learning
Alberto Dalla Libera, Giulio Giacomuzzo, Ruggero Carli +2
In this paper, we propose to estimate the forward dynamics equations of mechanical systems by learning a model of the inverse dynamics and estimating individual dynamics components…
Learning Control from Raw Position Measurements
Fabio Amadio, Alberto Dalla Libera, Daniel Nikovski +2
We propose a Model-Based Reinforcement Learning (MBRL) algorithm named VF-MC-PILCO, specifically designed for application to mechanical systems where velocities cannot be directly…
Internal Model-Based Online Optimization
Nicola Bastianello, Ruggero Carli, Sandro Zampieri
In this paper we propose a model-based approach to the design of online optimization algorithms, with the goal of improving the tracking of the solution trajectory (trajectories) w…
Control of over-redundant cooperative manipulation via sampled communication
Enrica Rossi, Marco Tognon, Ruggero Carli +2
In this work we consider the problem of mobile robots that need to manipulate/transport an object via cables or robotic arms. We consider the scenario where the number of manipulat…
Coordinated Multi-Robot Trajectory Tracking Control over Sampled Communication
Enrica Rossi, Marco Tognon, Luca Ballotta +4
In this paper, we propose an inverse-kinematics controller for a class of multi-robot systems in the scenario of sampled communication. The goal is to make a group of robots perfor…
EASE: Energy-Aware Job Scheduling for Vehicular Edge Networks With Renewable Energy Resources
Giovanni Perin, Francesca Meneghello, Ruggero Carli +2
The energy sustainability of multi-access edge computing (MEC) platforms is here addressed by developing Energy-Aware job Scheduling at the Edge (EASE), a computing resource schedu…
Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models
Alberto Dalla Libera, Fabio Amadio, Daniel Nikovski +2
In this paper, we consider the use of black-box Gaussian process (GP) models for trajectory tracking control based on feedback linearization, in the context of mechanical systems.…
A Stochastic Operator Framework for Optimization and Learning with Sub-Weibull Errors
Nicola Bastianello, Liam Madden, Ruggero Carli +1
This paper proposes a framework to study the convergence of stochastic optimization and learning algorithms. The framework is modeled over the different challenges that these algor…
Model-based Policy Search for Partially Measurable Systems
Fabio Amadio, Alberto Dalla Libera, Ruggero Carli +2
In this paper, we propose a Model-Based Reinforcement Learning (MBRL) algorithm for Partially Measurable Systems (PMS), i.e., systems where the state can not be directly measured,…
Model-Based Policy Search Using Monte Carlo Gradient Estimation with Real Systems Application
Fabio Amadio, Alberto Dalla Libera, Riccardo Antonello +3
In this paper, we present a Model-Based Reinforcement Learning (MBRL) algorithm named \emph{Monte Carlo Probabilistic Inference for Learning COntrol} (MC-PILCO). The algorithm reli…
Distributed Prediction-Correction ADMM for Time-Varying Convex Optimization
Nicola Bastianello, Andrea Simonetto, Ruggero Carli
This paper introduces a dual-regularized ADMM approach to distributed, time-varying optimization. The proposed algorithm is designed in a prediction-correction framework, in which…
Extrapolation-based Prediction-Correction Methods for Time-varying Convex Optimization
Nicola Bastianello, Ruggero Carli, Andrea Simonetto
In this paper, we focus on the solution of online optimization problems that arise often in signal processing and machine learning, in which we have access to streaming sources of…