Giancarlo Ferrari Trecate

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We study control of constrained linear systems with only partial statistical information about the uncertainty affecting the system dynamics and the sensor measurements. Specifically, given a finite collection of disturbance realizations drawn from a gener ...

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 system matrices are known, from which the spectral radius can be directly comput ...

This paper defines a Maximum Likelihood Estimator (MLE) for the admittance matrix estimation of distribution grids, utilising voltage magnitude and power measurements collected only from common, unsychronised measuring devices (Smart Meters). First, we pre ...

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. State observers based on the minimization of regret measures ar ...

The paper considers the problem of detecting cyber-attacks occurring in communication networks typically used in the secondary control layer of DC microgrids. The proposed distributed methodology allows for scalable monitoring of a microgrid and is able to ...

In this paper we propose an efficient procedure to compute time-varying Robust Control Invariant (RCI) sets for large-scale systems arising from the interconnection of M Linear Time-Invariant (LTI) constrained subsystems. In particular, the associated stat ...

This letter investigates the universal approximation capabilities of Hamiltonian Deep Neural Networks (HDNNs) that arise from the discretization of Hamiltonian Neural Ordinary Differential Equations. Recently, it has been shown that HDNNs enjoy, by design, ...

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. We study the consensusability of linear interconnected MASs (LIMASs) where, as in sever ...

In this paper, we propose a new hierarchical control scheme for microgrid (MG) clusters, given by the interconnection of atomic dc MGs with ZIP loads, each composed by both grid-forming and grid-feeding converters. In the primary level, we develop a new Pl ...

This paper proposes a novel approach to improve the performance of distributed nonlinear control systems while preserving stability by leveraging Deep Neural Networks (DNNs). We build upon the Neural System Level Synthesis (Neur-SLS) framework and introduc ...

Controlling large-scale cyber-physical systems necessitates optimal distributed policies, relying solely on local real-time data and limited communication with neighboring agents. However, finding optimal controllers remains challenging, even in seemingly ...

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. However, the spatiotemporal imbalances between mobility demand and supply ...

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 the secure region. Employing droop voltage regulators on the RES's inverter ...

Kron reduction (KR) is a methodology for analyzing an electrical network by replacing it with a simpler circuit having less nodes but the same terminal behavior of voltages and currents at target vertices. Existing approaches to instantaneous KR, however, ...

The paper considers the problem of detecting cyber-attacks occurring in communication networks for distributed control schemes. A distributed methodology is proposed to detect the presence of malicious attacks aimed at compromising the stability of large-s ...

Machine Learning (ML) and linear System Identification (SI) have been historically developed independently. In this paper, we leverage well-established ML tools - especially the automatic differentiation framework - to introduce SIMBa, a family of discrete ...

This paper investigates the application of passivity-based nonlinear control to the problem of primary voltage stabilization in medium-voltage DC microgrids (MVDC mGs) given by the interconnection of nonlinear distributed generation units (DGUs) and power ...

In this paper, we investigate the stability of an islanded AC microgrid (ImG) composed of Distributed generation units (DGUs), dynamic power lines, and loads. Each DGU is equipped with a local controller to ensure desired voltage and frequency in the ImG. ...

This paper proposes a novel scalable model-based fault detection and isolation approach for the monitoring of nonlinear large-scale systems, consisting of a network of interconnected subsystems. The fault diagnosis architecture is designed to automatically ...

This manuscript details and extends the system identification methods leveraging the backpropagation (SIMBa) toolbox presented in previous work, which uses well-established machine learning tools for discrete-time linear multistep-ahead state-space system ...

Giancarlo Ferrari Trecate

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Distributionally robust infinite-horizon control: from a pool of samples to the design of dependable controllers

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

Maximum likelihood estimation of distribution grid topology and parameters from Smart Meter data

Minimal regret state estimation of time-varying systems

Distributed Cyber-Attack Detection in the Secondary Control of DC Microgrids

Decentralized and distributed robust control invariance for constrained linear systems

Universal Approximation Property of Hamiltonian Deep Neural Networks

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

Stability Analysis of Primary Plug-and-Play and Secondary Leader-Based Controllers for DC Microgrid Clusters

Optimal distributed control with stability guarantees by training a network of neural closed-loop maps

Neural Distributed Controllers with Port-Hamiltonian Structures

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

Optimal Adaptive Droop Design via a Modified Relaxation of the OPF

Approximate Kron Reduction Methods for Electrical Networks With Applications to Plug-and-Play Control of AC Islanded Microgrids

A distributed attack detection method for multi-agent systems governed by consensus-based control

Stable Linear Subspace Identification: A Machine Learning Approach

Voltage Stabilization in MVDC Microgrids Using Passivity-Based Nonlinear Control

Passivity-based Voltage and Frequency Stabilization in AC microgrids

Plug-and-Play Fault Detection and Isolation for Large-Scale Nonlinear Systems With Stochastic Uncertainties

SIMBa: System Identification Methods Leveraging Backpropagation