26 citations · 52 across the 22 of their papers we have counts for
24 papers
Temporal Variabilities Limit Convergence Rates in Gradient-Based Online Optimization
Bryan Van Scoy, Gianluca Bianchin
This paper investigates the fundamental performance limits of gradient-based algorithms for time-varying optimization. Leveraging the internal model principle and root locus techni…
Feedback Optimization of Dynamical Systems in Time-Varying Environments: An Internal Model Principle Approach
Gianluca Bianchin, Bryan Van Scoy
Feedback optimization has emerged as a promising approach for regulating dynamical systems to optimal steady states that are implicitly defined by underlying optimization problems.…
The Internal Model Principle of Time-Varying Optimization
Gianluca Bianchin, Bryan Van Scoy
Time-varying optimization problems are central to many engineering applications, where performance metrics and system constraints evolve dynamically with time. Several algorithms h…
Optimization of Linear Multi-Agent Dynamical Systems via Feedback Distributed Gradient Descent Methods
Amir Mehrnoosh, Gianluca Bianchin
Feedback optimization is an increasingly popular control paradigm to optimize dynamical systems, accounting for control objectives that concern the system operation at steady-state…
Renewable-Based Charging in Green Ride-Sharing
Elisabetta Perotti, Ana M. Ospina, Gianluca Bianchin +2
Governments, regulatory bodies, and manufacturers are proposing plans to accelerate the adoption of electric vehicles (EVs), with the goal of reducing the impact of greenhouse gase…
Data-Driven Exact Pole Placement for Linear Systems
Gianluca Bianchin
The exact pole placement problem concerns computing a feedback gain that will assign the poles of a system, controlled via static state feedback, at a set of pre-specified location…
Perception-Based Sampled-Data Optimization of Dynamical Systems
Liliaokeawawa Cothren, Gianluca Bianchin, Sarah Dean +1
Motivated by perception-based control problems in autonomous systems, this paper addresses the problem of developing feedback controllers to regulate the inputs and the states of a…
k-Dimensional Agreement in Multiagent Systems
Gianluca Bianchin, Miguel Vaquero, Jorge Cortes +1
Given a network of agents, we study the problem of designing a distributed algorithm that computes k independent weighted means of the network's initial conditions (namely, the age…
Online Optimization of Dynamical Systems with Deep Learning Perception
Liliaokeawawa Cothren, Gianluca Bianchin, Emiliano Dall'Anese
This paper considers the problem of controlling a dynamical system when the state cannot be directly measured and the control performance metrics are unknown or partially known. In…
Stability analysis of an eight parameter SIR-type model including loss of immunity, and disease and vaccination fatalities
Florin Avram, Rim Adenane, Gianluca Bianchin +1
We revisit here a landmark five parameter SIR-type model of [DvdD93, Sec. 4], which is maybe the simplest example where a complete picture of all cases, including non-trivial bista…
On matrix-SIR Arino models with linear birth rate, loss of immunity, disease and vaccination fatalities, and their approximations
Florin Avram, Rim Adenane, Lasko Basnarkov +3
In this work we study the stability properties of the equilibrium points of deterministic epidemic models with nonconstant population size. Models with nonconstant population have…
Data-Enabled Gradient Flow as Feedback Controller: Regulation of Linear Dynamical Systems to Minimizers of Unknown Functions
Liliaokeawawa Cothren, Gianluca Bianchin, Emiliano Dall'Anese
This paper considers the problem of regulating a linear dynamical system to the solution of a convex optimization problem with an unknown or partially-known cost. We design a data-…
Planning a Return to Normal after the COVID-19 Pandemic: Identifying Safe Contact Levels via Online Optimization
Gianluca Bianchin, Emiliano Dall'Anese, Jorge I. Poveda +3
Since the early months of 2020, non-pharmaceutical interventions (NPIs) -- implemented at varying levels of severity and based on widely-divergent perspectives of risk tolerance --…
Online Stochastic Optimization for Unknown Linear Systems: Data-Driven Synthesis and Controller Analysis
Gianluca Bianchin, Miguel Vaquero, Jorge Cortes +1
This paper proposes a data-driven control framework to regulate an unknown, stochastic linear dynamical system to the solution of a (stochastic) convex optimization problem. Despit…
Online Projected Gradient Descent for Stochastic Optimization with Decision-Dependent Distributions
Killian Wood, Gianluca Bianchin, Emiliano Dall'Anese
This paper investigates the problem of tracking solutions of stochastic optimization problems with time-varying costs that depend on random variables with decision-dependent distri…
Data-Driven Synthesis of Optimization-Based Controllers for Regulation of Unknown Linear Systems
Gianluca Bianchin, Miguel Vaquero, Jorge Cortes +1
This paper proposes a data-driven framework to solve time-varying optimization problems associated with unknown linear dynamical systems. Making online control decisions to regulat…
Online Optimization of LTI Systems Under Persistent Attacks: Stability, Tracking, and Robustness
Felipe Galarza-Jimenez, Gianluca Bianchin, Jorge I. Poveda +1
We study the stability properties of a control system composed of a dynamical plant and a feedback controller, the latter generating control signals that can be compromised by a ma…
Time-Varying Optimization of LTI Systems via Projected Primal-Dual Gradient Flows
Gianluca Bianchin, Jorge Cortes, Jorge I. Poveda +1
This paper investigates the problem of regulating in real time a linear dynamical system to the solution trajectory of a time-varying constrained convex optimization problem. The p…
Online Optimization of Switched LTI Systems Using Continuous-Time and Hybrid Accelerated Gradient Flows
Gianluca Bianchin, Jorge I. Poveda, Emiliano Dall'Anese
This paper studies the design of feedback controllers to steer a switching linear time-invariant dynamical system towards the solution trajectory of a time-varying convex optimizat…
Navigation Systems May Deteriorate Stability in Traffic Networks
Gianluca Bianchin, Fabio Pasqualetti
Advanced traffic navigation systems, which provide routing recommendations to drivers based on real-time congestion information, are nowadays widely adopted by roadway transportati…
Resilience of Traffic Networks with Partially Controlled Routing
Gianluca Bianchin, Fabio Pasqualetti, Soumya Kundu
This paper investigates the use of Infrastructure-To-Vehicle (I2V) communication to generate routing suggestions for drivers in transportation systems, with the goal of optimizing…
Secure Trajectory Planning Against Undetectable Spoofing Attacks
Yin-Chen Liu, Gianluca Bianchin, Fabio Pasqualetti
This paper studies, for the first time, the trajectory planning problem in adversarial environments, where the objective is to design the trajectory of a robot to reach a desired f…
Gramian-Based Optimization for the Analysis and Control of Traffic Networks
Gianluca Bianchin, Fabio Pasqualetti
This paper proposes a simplified version of classical models for urban transportation networks, and studies the problem of controlling intersections with the goal of optimizing net…
The Observability Radius of Networks
Gianluca Bianchin, Paolo Frasca, Andrea Gasparri +1
This paper studies the observability radius of network systems, which measures the robustness of a network to perturbations of the edges. We consider linear networks, where the dyn…