Publications by Andrea Martin | EPFL Graph Search

Optimal Control under Uncertainty: From Regret Minimization to Distributional Robustness

Recent years have witnessed a growing integration of automatic control into a wide range of cyber-physical systems, including electrical grids and autonomous transportation networks. These systems operate in uncertain and dynamic environments, exhibit comp ...

EPFL2025

On the Guarantees of Minimizing Regret in Receding Horizon

Giancarlo Ferrari Trecate, Luca Furieri, Andrea Martin

Toward bridging classical optimal control and online learning, regret minimization has recently been proposed as a control design criterion. This competitive paradigm penalizes the loss relative to the optimal control actions chosen by a clairvoyant policy ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2025

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

Jean-Sébastien Hubert Brouillon, Giancarlo Ferrari Trecate, Andrea Martin

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 ...

Institute of Electrical and Electronics Engineers (IEEE)2025

Learning to Optimize with Convergence Guarantees Using Nonlinear System Theory

Luca Furieri, Andrea Martin

The increasing reliance on numerical methods for controlling dynamical systems and training machine learning models underscores the need to devise algorithms that dependably and efficiently navigate complex optimization landscapes. Classical gradient desce ...

2024

Regret optimal control for uncertain stochastic systems

Giancarlo Ferrari Trecate, Luca Furieri, Andrea Martin

We consider control of uncertain linear time-varying stochastic systems from the perspective of regret minimization. Specifically, we focus on the problem of designing a feedback controller that minimizes the loss relative to a clairvoyant optimal policy t ...

ELSEVIER2024

Follow the Clairvoyant: an Imitation Learning Approach to Optimal Control

John Lygeros, Giancarlo Ferrari Trecate, Luca Furieri, Andrea Martin, Florian Dörfler

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 clairvoyant policy that has noncausal access to past, present, and future d ...

Elsevier2023

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

Baiwei Guo, Giancarlo Ferrari Trecate, Luca Furieri, Andrea Martin

As we transition toward the deployment of data-driven controllers for black-box cyberphysical systems, complying with hard safety constraints becomes a primary concern. Two key aspects should be addressed when input-output data are corrupted by noise: how ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Learning Robustly Safe Output Feedback Controllers from Noisy Data with Performance Guarantees

Baiwei Guo, Giancarlo Ferrari Trecate, Luca Furieri, Andrea Martin

How can we synthesize a safe and near-optimal control policy for a partially-observed system, if all we are given is one historical input/output trajectory that has been corrupted by noise? To address this challenge, we suggest a novel data-driven controll ...

2021

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

Baiwei Guo, Giancarlo Ferrari Trecate, Luca Furieri, Andrea Martin

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. Despite recent advances, a key aspect remains un ...

IEEE2021