Search Results for author: Mario di Bernardo

Found 24 papers, 4 papers with code

High-dimensional continuification control of large-scale multi-agent systems under limited sensing and perturbations

no code implementations25 Mar 2024 Gian Carlo Maffettone, Mario di Bernardo, Maurizio Porfiri

This paper investigates the robustness of a novel high-dimensional continuification control method for complex multi-agent systems.

Data-driven architecture to encode information in the kinematics of robots and artificial avatars

no code implementations11 Mar 2024 Francesco De Lellis, Marco Coraggio, Nathan C. Foster, Riccardo Villa, Cristina Becchio, Mario di Bernardo

We present a data-driven control architecture for modifying the kinematics of robots and artificial avatars to encode specific information such as the presence or not of an emotion in the movements of an avatar or robot driven by a human operator.

In vivo learning-based control of microbial populations density in bioreactors

no code implementations15 Dec 2023 Sara Maria Brancato, Davide Salzano, Francesco De Lellis, Davide Fiore, Giovanni Russo, Mario di Bernardo

Our work showcases the viability of learning-based strategies for the control of cellular density in bioreactors, making a step forward toward their use for the control of the composition of microbial consortia.

Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learning

1 code implementation16 Nov 2023 Francesco De Lellis, Marco Coraggio, Giovanni Russo, Mirco Musolesi, Mario di Bernardo

In addressing control problems such as regulation and tracking through reinforcement learning, it is often required to guarantee that the acquired policy meets essential performance and stability criteria such as a desired settling time and steady-state error prior to deployment.

OpenAI Gym reinforcement-learning

Data-driven design of complex network structures to promote synchronization

no code implementations19 Sep 2023 Marco Coraggio, Mario di Bernardo

We consider the problem of optimizing the interconnection graphs of complex networks to promote synchronization.

Preliminary results on the herdability of complex multiagent systems via local information

no code implementations22 Apr 2023 Andrea Lama, Mario di Bernardo

We present preliminary results on the problem of driving the dynamics of a group of agents, the herders, so as to steer the collective behaviour of another group of agents, the targets, interacting with them.

Continuification control of large-scale multiagent systems under limited sensing and structural perturbations

no code implementations23 Mar 2023 Gian Carlo Maffettone, Maurizio Porfiri, Mario di Bernardo

We investigate the stability and robustness properties of a continuification-based strategy for the control of large-scale multiagent systems.

Local convergence of multi-agent systems towards triangular patterns

1 code implementation21 Mar 2023 Andrea Giusti, Marco Coraggio, Mario di Bernardo

Geometric pattern formation is an important emergent behavior in many applications involving large-scale multi-agent systems, such as sensor networks deployment and collective transportation.

Consensus-based Distributed Intentional Controlled Islanding of Power Grids

no code implementations3 Jan 2023 Francesco Lo Iudice, Ricardo Cardona-Rivera, Antonio Grotta, Marco Coraggio, Mario di Bernardo

The problem of partitioning a power grid into a set of islands can be a solution to restore power dispatchment in sections of a grid affected by an extreme failure.

CT-DQN: Control-Tutored Deep Reinforcement Learning

no code implementations2 Dec 2022 Francesco De Lellis, Marco Coraggio, Giovanni Russo, Mirco Musolesi, Mario di Bernardo

One of the major challenges in Deep Reinforcement Learning for control is the need for extensive training to learn the policy.

Car Racing OpenAI Gym +2

Continuification-based control of large multiagent systems in a ring

no code implementations29 Sep 2022 Gian Carlo Maffettone, Alain Boldini, Mario di Bernardo, Maurizio Porfiri

In this paper, we propose a method to control large-scale multiagent systems swarming in a ring.

Predicting and Understanding Human Action Decisions during Skillful Joint-Action via Machine Learning and Explainable-AI

1 code implementation6 Jun 2022 Fabrizia Auletta, Rachel W. Kallen, Mario di Bernardo, Micheal J. Richardson

This study uses supervised machine learning (SML) and explainable artificial intelligence (AI) to model, predict and understand human decision-making during skillful joint-action.

Decision Making Explainable artificial intelligence

External control of a genetic toggle switch via Reinforcement Learning

no code implementations11 Apr 2022 Sara Maria Brancato, Francesco De Lellis, Davide Salzano, Giovanni Russo, Mario di Bernardo

We investigate the problem of using a learning-based strategy to stabilize a synthetic toggle switch via an external control approach.

reinforcement-learning Reinforcement Learning (RL)

Minimax Flow over Acyclic Networks: Distributed Algorithms and Microgrid Application

no code implementations10 Jan 2022 Marco Coraggio, Saber Jafarpour, Francesco Bullo, Mario di Bernardo

Given a flow network with variable suppliers and fixed consumers, the minimax flow problem consists in minimizing the maximum flow between nodes, subject to flow conservation and capacity constraints.

Adaptive and quasi-sliding control of shimmy in landing gears

no code implementations12 Nov 2021 Daniel A. Burbano-Lombana, Marco Coraggio, Mario di Bernardo, Franco Garofalo, Michele Pugliese

Shimmy is a dangerous phenomenon that occurs when aircraft's nose landing gears oscillate in a rapid and uncontrollable fashion.

Synchronization of networks of piecewise-smooth systems

no code implementations12 Nov 2021 Marco Coraggio, Pietro DeLellis, S. John Hogan, Mario di Bernardo

We study convergence in networks of piecewise-smooth (PWS) systems that commonly arise in applications to model dynamical systems whose evolution is affected by macroscopic events such as switches and impacts.

Utilizing synchronization to partition power networks into microgrids

no code implementations26 Jul 2021 Ricardo Cardona-Rivera, Francesco Lo Iudice, Antonio Grotta, Marco Coraggio, Mario di Bernardo

The problem of partitioning a power grid into a set of microgrids, or islands, is of interest for both the design of future smart grids, and as a last resort to restore power dispatchment in sections of a grid affected by an extreme failure.

Combinatorial Optimization

Intermittent non-pharmaceutical strategies to mitigate the COVID-19 epidemic in a network model of Italy via constrained optimization

1 code implementation25 Mar 2021 Marco Coraggio, Shihao Xie, Francesco De Lellis, Giovanni Russo, Mario di Bernardo

This paper is concerned with the design of intermittent non-pharmaceutical strategies to mitigate the spread of the COVID-19 epidemic exploiting network epidemiological models.

Model Predictive Control Unity

Intermittent yet coordinated regional strategies can alleviate the COVID-19 epidemic: a network model of the Italian case

no code implementations15 May 2020 Fabio Della Rossa, Davide Salzano, Anna Di Meglio, Francesco De Lellis, Marco Coraggio, Carmela Calabrese, Agostino Guarino, Ricardo Cardona, Pietro DeLellis, Davide Liuzza, Francesco Lo Iudice, Giovanni Russo, Mario di Bernardo

Using the model, we confirm the effectiveness at the regional level of the national lockdown strategy implemented so far by the Italian government to mitigate the spread of the disease and show its efficacy at the regional level.

Physics and Society Populations and Evolution 93C10, 92D30, 92D25 J.2

Control-Tutored Reinforcement Learning: an application to the Herding Problem

no code implementations26 Nov 2019 Francesco De Lellis, Fabrizia Auletta, Giovanni Russo, Mario di Bernardo

In this extended abstract we introduce a novel control-tutored Q-learning approach (CTQL) as part of the ongoing effort in developing model-based and safe RL for continuous state spaces.

Q-Learning reinforcement-learning +1

Control of Painlevé Paradox in a Robotic System

no code implementations9 Jul 2019 Davide Marchese, Marco Coraggio, S. John Hogan, Mario di Bernardo

The Painlev\'e paradox is a phenomenon that causes instability in mechanical systems subjects to unilateral constraints.

Deep learning control of artificial avatars in group coordination tasks

no code implementations11 Jun 2019 Maria Lombardi, Davide Liuzza, Mario di Bernardo

In many joint-action scenarios, humans and robots have to coordinate their movements to accomplish a given shared task.

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