433 citations · 658 across the 44 of their papers we have counts for
49 papers
Moving horizon partition-based state estimation of large-scale systems -- Revised version
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-overlappin…
Minimal regret state estimation of time-varying systems
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…
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…
Physically Consistent Neural ODEs for Learning Multi-Physics Systems
Muhammad Zakwan, Loris Di Natale, Bratislav Svetozarevic +3
Despite the immense success of neural networks in modeling system dynamics from data, they often remain physics-agnostic black boxes. In the particular case of physical systems, th…
Maximum likelihood estimation of distribution grid topology and parameters from smart meter data
Lisa Laurent, Jean-Sébastien Brouillon, Giancarlo Ferrari-Trecate
This paper defines a Maximum Likelihood Estimator (MLE) for the admittance matrix estimation of distribution grids, utilising voltage magnitude and power measurements collected onl…
Optimal droop control placement in distribution network via an exact OPF relaxation method
H. Sekhavatmanesh, G. Ferrari-Trecate, S. Mastellone
In the last decade, the integration of Renewable Energy Sources (RES) in distribution networks has been constantly increasing due to their many technical, economical, and environme…
Robust online joint state/input/parameter estimation of linear systems
Jean-Sébastien Brouillon, Keith Moffat, Florian Dörfler +1
This paper presents a method for jointly estimating the state, input, and parameters of linear systems in an online fashion. The method is specially designed for measurements that…
Optimal Adaptive Droop Design via a Modified Relaxation of the OPF
H. Sekhavatmanesh, G. Ferrari-Trecate, S. Mastellone
The ever increasing penetration of Renewable Energy Resources (RESs) in power distribution networks has brought, among others, the challenge of maintaining the grid voltages within…
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…
Robust Classification using Contractive Hamiltonian Neural ODEs
Muhammad Zakwan, Liang Xu, Giancarlo Ferrari-Trecate
Deep neural networks can be fragile and sensitive to small input perturbations that might cause a significant change in the output. In this paper, we employ contraction theory to i…
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…
Neural Energy Casimir Control for Port-Hamiltonian Systems
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. However, its application relies on the availabi…
Bayesian Error-in-Variables Models for the Identification of Power Networks
Jean-Sébastien Brouillon, Emanuele Fabbiani, Pulkit Nahata +3
The increasing integration of intermittent renewable generation, especially at the distribution level,necessitates advanced planning and optimisation methodologies contingent on th…
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…
Data-driven Unknown-input Observers and State Estimation
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. In this paper, we provide a novel data-driven UIO based on behav…
A unified framework for Hamiltonian deep neural networks
Clara L. Galimberti, Liang Xu, Giancarlo Ferrari Trecate
Training deep neural networks (DNNs) can be difficult due to the occurrence of vanishing/exploding gradients during weight optimization. To avoid this problem, we propose a class o…
Actuator Placement for Structural Controllability beyond Strong Connectivity and towards Robustness
Baiwei Guo, Orcun Karaca, Sepide Azhdari +2
Actuator placement is a fundamental problem in control design for large-scale networks. In this paper, we study the problem of finding a set of actuator positions by minimizing a g…
A Unified Passivity-Based Framework for Control of Modular Islanded AC Microgrids
Felix Strehle, Pulkit Nahata, Albertus Johannes Malan +2
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. This paper puts…
A Behavioral Input-Output Parametrization of Control Policies with Suboptimality Guarantees
Luca Furieri, Baiwei Guo, Andrea Martin +1
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 int…
Finite-sample-based Spectral Radius Estimation and Stabilizability Test for Networked Control Systems
Liang Xu, Baiwei Guo, 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. Usually, it is assumed that syst…
A Data-Driven Convex Programming Approach to Worst-Case Robust Tracking Controller Design
Liang Xu, Mustafa Sahin Turan, Baiwei Guo +1
This paper studies finite-horizon robust tracking control for discrete-time linear systems, based on input-output data. We leverage behavioral theory to represent system trajectori…
Non-conservative Design of Robust Tracking Controllers Based on Input-output Data
Liang Xu, Mustafa Sahin Turan, Baiwei Guo +1
This paper studies worst-case robust optimal tracking using noisy input-output data. We utilize behavioral system theory to represent system trajectories, while avoiding explicit s…