Search Results for author: Giancarlo Ferrari-Trecate

Found 36 papers, 18 papers with code

Learning to Boost the Performance of Stable Nonlinear Systems

1 code implementation1 May 2024 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 through state-of-the-art optimization and machine learning algorithms.

Neural Distributed Controllers with Port-Hamiltonian Structures

1 code implementation26 Mar 2024 Muhammad Zakwan, Giancarlo Ferrari-Trecate

Controlling large-scale cyber-physical systems necessitates optimal distributed policies, relying solely on local real-time data and limited communication with neighboring agents.

A PAC-Bayesian Framework for Optimal Control with Stability Guarantees

1 code implementation26 Mar 2024 Mahrokh Ghoddousi Boroujeni, Clara Lucía Galimberti, Andreas Krause, Giancarlo Ferrari-Trecate

Based on these bounds, we propose a new method for designing optimal controllers, offering a principled way to incorporate prior knowledge into the synthesis process, which aids in improving the control policy and mitigating overfitting.

Generalization Bounds

Neural Exponential Stabilization of Control-affine Nonlinear Systems

1 code implementation26 Mar 2024 Muhammad Zakwan, Liang Xu, Giancarlo Ferrari-Trecate

Third, this parametrization and the inequality condition enable the design of contractivity-enforcing regularizers, which can be incorporated while designing the NN controller for exponential stabilization of the underlying nonlinear systems.

Moving horizon partition-based state estimation of large-scale systems -- Revised version

no code implementations31 Jan 2024 Marcello Farina, Giancarlo Ferrari-Trecate, Riccardo Scattolini

This report presents three Moving Horizon Estimation (MHE) methods for discrete-time partitioned linear systems, i. e. systems decomposed into coupled subsystems with non-overlapping states.

Power Grid Parameter Estimation Without Phase Measurements: Theory and Empirical Validation

no code implementations18 Jan 2024 Jean-Sébastien Brouillon, Keith Moffat, Florian Dörfler, Giancarlo Ferrari-Trecate

The primary result of the paper is that, while the impedance of a line or a network can be estimated without synchronized phase angle measurements in a consistent way, the admittance cannot.

SIMBa: System Identification Methods leveraging Backpropagation

1 code implementation23 Nov 2023 Loris Di Natale, Muhammad Zakwan, Philipp Heer, Giancarlo Ferrari-Trecate, Colin N. Jones

This manuscript details and extends the SIMBa toolbox (System Identification Methods leveraging Backpropagation) presented in previous work, which uses well-established Machine Learning tools for discrete-time linear multi-step-ahead state-space System Identification (SI).

Unconstrained learning of networked nonlinear systems via free parametrization of stable interconnected operators

1 code implementation23 Nov 2023 Leonardo Massai, Danilo Saccani, Luca Furieri, Giancarlo Ferrari-Trecate

Further, we can embed prior knowledge about the interconnection topology and stability properties of the system directly into the large-scale distributed operator we design.

A Coverage Control-based Idle Vehicle Rebalancing Approach for Autonomous Mobility-on-Demand Systems

no code implementations20 Nov 2023 Pengbo Zhu, Isik Ilber Sirmatel, Giancarlo Ferrari-Trecate, Nikolas Geroliminis

As an emerging mode of urban transportation, Autonomous Mobility-on-Demand (AMoD) systems show the potential in improving mobility in cities through timely and door-to-door services.

Management

Stable Linear Subspace Identification: A Machine Learning Approach

1 code implementation6 Nov 2023 Loris Di Natale, Muhammad Zakwan, Bratislav Svetozarevic, Philipp Heer, Giancarlo Ferrari-Trecate, Colin N. Jones

Machine Learning (ML) and linear System Identification (SI) have been historically developed independently.

On the Guarantees of Minimizing Regret in Receding Horizon

no code implementations26 Jun 2023 Andrea Martin, Luca Furieri, Florian Dörfler, John Lygeros, Giancarlo Ferrari-Trecate

Towards bridging classical optimal control and online learning, regret minimization has recently been proposed as a control design criterion.

Regret Optimal Control for Uncertain Stochastic Systems

1 code implementation28 Apr 2023 Andrea Martin, Luca Furieri, Florian Dörfler, John Lygeros, Giancarlo Ferrari-Trecate

Specifically, we focus on the problem of designing a feedback controller that minimizes the loss relative to a clairvoyant optimal policy that has foreknowledge of both the system dynamics and the exogenous disturbances.

Regularization for distributionally robust state estimation and prediction

no code implementations19 Apr 2023 Jean-Sébastien Brouillon, Florian Dörfler, Giancarlo Ferrari-Trecate

We provide a direct method using samples of the noise to create a moving horizon observer for linear time-varying and nonlinear systems, which is optimal under the empirical noise distribution.

Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations

1 code implementation6 Apr 2023 Daniele Martinelli, Clara Lucía Galimberti, Ian R. Manchester, Luca Furieri, Giancarlo Ferrari-Trecate

We validate the properties of NodeRENs, including the possibility of handling irregularly sampled data, in a case study in nonlinear system identification.

Universal Approximation Property of Hamiltonian Deep Neural Networks

no code implementations21 Mar 2023 Muhammad Zakwan, Massimiliano d'Angelo, Giancarlo Ferrari-Trecate

This paper investigates the universal approximation capabilities of Hamiltonian Deep Neural Networks (HDNNs) that arise from the discretization of Hamiltonian Neural Ordinary Differential Equations.

Minimal regret state estimation of time-varying systems

no code implementations25 Nov 2022 Jean-Sébastien Brouillon, Florian Dörfler, Giancarlo Ferrari-Trecate

Kalman and H-infinity filters, the most popular paradigms for linear state estimation, are designed for very specific specific noise and disturbance patterns, which may not appear in practice.

Maximum likelihood estimation of distribution grid topology and parameters from smart meter data

no code implementations5 Oct 2022 Lisa Laurent, Jean-Sébastien Brouillon, Giancarlo Ferrari-Trecate

Not measuring the voltage phase only adds 30\% of error to the admittance matrix estimate in realistic conditions.

Robust online joint state/input/parameter estimation of linear systems

no code implementations12 Apr 2022 Jean-Sébastien Brouillon, Keith Moffat, Florian Dörfler, Giancarlo Ferrari-Trecate

This paper presents a method for jointly estimating the state, input, and parameters of linear systems in an online fashion.

regression

Robust Classification using Contractive Hamiltonian Neural ODEs

1 code implementation22 Mar 2022 Muhammad Zakwan, Liang Xu, Giancarlo Ferrari-Trecate

Since in NODEs the input data corresponds to the initial condition of dynamical systems, we show contractivity can mitigate the effect of input perturbations.

Classification Image Classification +1

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

1 code implementation22 Mar 2022 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.

Safe Control with Minimal Regret

1 code implementation1 Mar 2022 Andrea Martin, Luca Furieri, Florian Dörfler, John Lygeros, Giancarlo Ferrari-Trecate

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 control algorithms and adaptive learning-based methods.

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

1 code implementation16 Dec 2021 Luca Furieri, Clara Lucía Galimberti, Muhammad Zakwan, Giancarlo Ferrari-Trecate

A main challenge of NN controllers is that they are not dependable during and after training, that is, the closed-loop system may be unstable, and the training may fail due to vanishing and exploding gradients.

Neural Energy Casimir Control for Port-Hamiltonian Systems

1 code implementation6 Dec 2021 Liang Xu, Muhammad Zakwan, Giancarlo Ferrari-Trecate

The energy Casimir method is an effective controller design approach to stabilize port-Hamiltonian systems at a desired equilibrium.

Non Vanishing Gradients for Arbitrarily Deep Neural Networks: a Hamiltonian System Approach

no code implementations NeurIPS Workshop DLDE 2021 Clara Galimberti, Luca Furieri, Liang Xu, Giancarlo Ferrari-Trecate

Deep Neural Networks (DNNs) training can be difficult due to vanishing or exploding gradients during weight optimization through backpropagation.

Bayesian Error-in-Variables Models for the Identification of Power Networks

no code implementations9 Jul 2021 Jean-Sébastien Brouillon, Emanuele Fabbiani, Pulkit Nahata, Keith Moffat, Florian Dörfler, Giancarlo Ferrari-Trecate

The increasing integration of intermittent renewable generation, especially at the distribution level, necessitates advanced planning and optimisation methodologies contingent on the knowledge of thegrid, specifically the admittance matrix capturing the topology and line parameters of an electricnetwork.

Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by Design

3 code implementations27 May 2021 Clara Lucía Galimberti, Luca Furieri, Liang Xu, Giancarlo Ferrari-Trecate

Deep Neural Networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation.

Image Classification

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

no code implementations21 May 2021 Luca Furieri, Baiwei Guo, Andrea Martin, Giancarlo Ferrari-Trecate

As we transition towards the deployment of data-driven controllers for black-box cyberphysical systems, complying with hard safety constraints becomes a primary concern.

Data-driven Unknown-input Observers and State Estimation

no code implementations20 May 2021 Mustafa Sahin Turan, Giancarlo Ferrari-Trecate

Unknown-input observers (UIOs) allow for estimation of the states of an LTI system without knowledge of all inputs.

Cyber Attack Detection LEMMA

A Unified Passivity-Based Framework for Control of Modular Islanded AC Microgrids

no code implementations6 Apr 2021 Felix Strehle, Pulkit Nahata, Albertus Johannes Malan, Sören Hohmann, Giancarlo Ferrari-Trecate

Voltage and frequency control in an islanded AC microgrid (ImGs) amount to stabilizing an a priori unknown ImG equilibrium induced by loads and changes in topology.

Finite-sample-based Spectral Radius Estimation and Stabilizability Test for Networked Control Systems

no code implementations3 Mar 2021 Liang Xu, Giancarlo Ferrari-Trecate

In the analysis and control of discrete-time linear time-invariant systems, the spectral radius of the system state matrix plays an essential role.

Optimization and Control Systems and Control Systems and Control

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

no code implementations26 Feb 2021 Luca Furieri, Baiwei Guo, Andrea Martin, Giancarlo Ferrari-Trecate

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.

On Consensusability of Linear Interconnected Multi-Agent Systems and Simultaneous Stabilization

no code implementations20 Oct 2020 Mustafa Sahin Turan, Liang Xu, Giancarlo Ferrari-Trecate

Consensusability of multi-agent systems (MASs) certifies the existence of a distributed controller capable of driving the states of each subsystem to a consensus value.

Identification of AC Networks via Online Learning

no code implementations13 Mar 2020 Emanuele Fabbiani, Pulkit Nahata, Giuseppe De Nicolao, Giancarlo Ferrari-Trecate

The increasing penetration of intermittent distributed energy resources in power networks calls for novel planning and control methodologies which hinge on detailed knowledge of the grid.

Hierarchical Control in Islanded DC Microgrids with Flexible Structures

1 code implementation11 Oct 2019 Pulkit Nahata, Alessio La Bella, Riccardo Scattolini, Giancarlo Ferrari-Trecate

Hierarchical architectures stacking primary, secondary, and tertiary layers are widely employed for the operation and control of islanded DC microgrids (DCmGs), composed of Distribution Generation Units (DGUs), loads, and power lines.

energy management Management

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