Saverio Bolognani

Last Name

Bolognani

First Name

Saverio

ORCID

0000-0002-7935-1385

Organisational unit

09478 - Dörfler, Florian / Dörfler, Florian

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Publications 1 - 10 of 80

Toward a Systems Theory of Algorithms
Dörfler, Florian; He, Zhiyu; Belgioioso, Giuseppe; et al. (2024)
IEEE Control Systems Letters
Traditionally, numerical algorithms are seen as isolated pieces of code confined to an in silico existence. However, this perspective is inappropriate for many modern computational approaches in control, learning, or optimization, wherein in vivo algorithms interact with their environment. Examples of such open algorithms include various real-time optimization-based control strategies, reinforcement learning, decision-making architectures, online optimization, and many more. Further, even closed algorithms in learning or optimization are increasingly abstracted in block diagrams with interacting dynamic modules and pipelines. In this opinion letter, we state our vision on a to-be-cultivated systems theory of algorithms and argue in favor of viewing algorithms as open dynamical systems interacting with other algorithms, physical systems, humans, or databases. Remarkably, the manifold tools developed under the umbrella of systems theory are well suited for addressing a rangeofchallenges in the algorithmic domain. We survey various instances where the principles of algorithmic systems theory are being developed and outline pertinent modeling, analysis, and design challenges.
Virtual Inertia Placement in Electric Power Grids
Poolla, Bala Kameshwar; Gross, Dominic; Borsche, Theodor; et al. (2018)
The IMA Volumes in Mathematics and its Applications ~ Energy Markets and Responsive Grids. Modeling, Control, and Optimization
Model-Free Nonlinear Feedback Optimization
He, Zhiyu; Bolognani, Saverio; He, Jianping; et al. (2024)
IEEE Transactions on Automatic Control
Feedback optimization is a control paradigm that enables physical systems to autonomously reach efficient operating points. Its central idea is to interconnect optimization iterations in closed-loop with the physical plant. Since iterative gradient-based methods are extensively used to achieve optimality, feedback optimization controllers typically require the knowledge of the steady-state sensitivity of the plant, which may not be easily accessible in some applications. In contrast, in this paper we develop a model-free feedback controller for efficient steady-state operation of general dynamical systems. The proposed design consists in updating control inputs via gradient estimates constructed from evaluations of the nonconvex objective at the current input and at the measured output. We study the dynamic interconnection of the proposed iterative controller with a stable nonlinear discrete-time plant. For this setup, we characterize the optimality and stability of the closed-loop behavior as functions of the problem dimension, the number of iterations, and the rate of convergence of the physical plant. To handle general constraints that affect multiple inputs, we enhance the controller with Frank-Wolfe-type updates.
Data-driven predictive current control for synchronous motor drives
Carlet, Paolo G.; Favato, Andrea; Bolognani, Saverio; et al. (2020)
2020 IEEE Energy Conversion Congress and Exposition (ECCE)
Data-driven control techniques have become increasingly popular in recent years due to the availability of massive amounts of data and several advances in data science. These control design methods bypass the system identification step and directly exploit collected data to construct the controller. In this paper, we investigate the application of data-driven methods to the control of electric motor drives, and specifically to the design of current controllers for three-phase synchronous permanent magnet motor drives. Two of the most promising data-driven algorithms are presented, namely the Subspace Predictive Control algorithm and the Data-Enabled Predictive Control algorithm. The theory behind these techniques is first reviewed in the optimization-based control framework. Standard algorithms are slightly modified to fulfill the requirements of the specific application, and then simulated in the MATLAB Simulink environment. Some key aspects of real-time implementation are studied, providing a proof-of-concept demonstration of the applicability of these algorithms. The data-driven design is proposed for three different topologies of synchronous motors, proving the flexibility of the approach. © 2020 IEEE
Distributed Control and Optimization for Autonomous Power Grids
Dörfler, Florian; Bolognani, Saverio; Simpson-Porco, John W.; et al. (2019)
Proceedings of the 18th European Control Conference (ECC 2019)
Online Feedback Equilibrium Seeking
Belgioioso, Giuseppe; Liao-McPherson, Dominic; Hudoba de Badyn, Mathias; et al. (2025)
IEEE Transactions on Automatic Control
This paper proposes a unifying design framework for dynamic feedback controllers that track solution trajectories of time-varying generalized equations, such as local minimizers of nonlinear programs or competitive equilibria (e.g., Nash) of non-cooperative games. Inspired by the feedback optimization paradigm, the core idea of the proposed approach is to re-purpose classic iterative algorithms for solving generalized equations (e.g., Josephy–Newton, forward-backward splitting) as dynamic feedback controllers by integrating online measurements of the continuous-time nonlinear plant. Sufficient conditions for closed-loop stability and robustness of the algorithm-plant cyber-physical interconnection are derived in a sampled-data setting by combining and tailoring results from (monotone) operator, fixed-point, and nonlinear systems theory. Numerical simulations on smart building automation and competitive supply-chain management are presented to support the theoretical findings.
A Fast Method for Real-Time Chance-Constrained Decision with Application to Power Systems
Bolognani, Saverio; Arcari, Elena; Dörfler, Florian (2017)
IEEE Control Systems Letters
To Travel Quickly or to Park Conveniently: Coupled Resource Allocations with Multi-Karma Economies
Elokda, Ezzat; Censi, Andrea; Bolognani, Saverio; et al. (2024)
IFAC-PapersOnLine ~ 5th IFAC Workshop on Cyber-Physical Human Systems CPHS 2024 Proceedings
The large-scale allocation of public resources (e.g., transportation, energy) is among the core challenges of future Cyber-Physical-Human Systems (CPHS). In order to guarantee that these systems are efficient and fair, recent works have investigated non-monetary resource allocation schemes, including schemes that employ karma. Karma is a non-tradable token that flows from users gaining resources to users yielding resources. Thus far karma-based solutions considered the allocation of a single public resource, however, modern CPHS are complex as they involve the allocation of multiple coupled resources. For example, a user might want to trade-of fast travel on highways for convenient parking in the city center, and different users could have heterogeneous preferences for such coupled resources. In this paper, we explore how to optimally combine multiple karma economies for coupled resource allocations, using two mechanism-design instruments: (non-uniform) karma redistribution; and (non-unit) exchange rates. We first extend the existing Dynamic Population Game (DPG) model that predicts the Stationary Nash Equilibrium (SNE) of the multi-karma economies. Then, in a numerical case study, we demonstrate that the design of redistribution significantly affects the coupled resource allocations, while non-unit exchange rates play a minor role. To assess the allocation outcomes under user heterogeneity, we adopt Nash welfare as our social welfare function, since it makes no interpersonal comparisons and it is axiomatically rooted in social choice theory. Our findings suggest that the simplest mechanism design, that is, uniform redistribution with unit exchange rates, also attains maximum social welfare.
Posetal Games: Efficiency, Existence, and Refinement of Equilibria in Games With Prioritized Metrics
Zanardi, Alessandro; Zardini, Gioele; Srinivasan, Sirish; et al. (2022)
IEEE Robotics and Automation Letters
Modern applications require robots to comply with multiple, often conflicting rules and to interact with the other agents. We present Posetal Games as a class of games in which each player expresses a preference over the outcomes via a partially ordered set of metrics. This allows one to combine hierarchical priorities of each player with the interactive nature of the environment. By contextualizing standard game theoretical notions, we provide two sufficient conditions on the preference of the players to prove existence of pure Nash Equilibria in finite action sets. Moreover, we define formal operations on the preference structures and link them to a refinement of the game solutions, showing how the set of equilibria can be systematically shrunk. The presented results are showcased in a driving game where autonomous vehicles select from a finite set of trajectories. The results demonstrate the interpretability of results in terms of minimum-rank-violation for each player.
Distributed reactive power feedback control for voltage regulation and loss minimization
Bolognani, Saverio; Carli, Ruggero; Cavraro, Guido; et al. (2015)
IEEE Transactions on Automatic Control

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