Publications by Baiwei Guo | EPFL Graph Search

Related publications (10)

Actuator Placement for Structural Controllability beyond Strong Connectivity and towards Robustness

Giancarlo Ferrari Trecate, Maryam Kamgarpour, Baiwei Guo

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 given metric, while satisfying a structural controllability requirement and a ...

IEEE2021

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

Baiwei Guo

Control systems operating in real-world environments often face disturbances arising from measurement noise and model mismatch. These factors can significantly impact the perfor- mance and safety of the system. In this thesis, we aim to leverage data to de ...

EPFL2023

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

Giancarlo Ferrari Trecate, Luca Furieri, Baiwei Guo, 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

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

Giancarlo Ferrari Trecate, Liang Xu, Baiwei Guo

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 ...

IEEE2022

Suboptimal Distributed LQR Design for Physically Coupled Systems

Giancarlo Ferrari Trecate, Liang Xu, Baiwei Guo, Clara Lucía Galimberti

In this paper, we propose a suboptimal distributed LQR control method, applicable to systems coupled through both physical interconnections and the quadratic cost to be minimized. Thanks to a novel suboptimal but distributed cost-to-go matrix update that e ...

ELSEVIER2020

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

Giancarlo Ferrari Trecate, Luca Furieri, Baiwei Guo, 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

Safe Zeroth-Order Convex Optimization Using Quadratic Local Approximations

Giancarlo Ferrari Trecate, Yuning Jiang, Maryam Kamgarpour, Baiwei Guo

We address black-box convex optimization problems, where the objective and constraint functions are not explicitly known but can be sampled within the feasible set. The challenge is thus to generate a sequence of feasible points converging towards an optim ...

IEEE2023

A comment on performance guarantees of a greedy algorithm for minimizing a supermodular set function on comatroid

Maryam Kamgarpour, Baiwei Guo

We provide a counterexample to the performance guarantee obtained in the paper "Il'ev, V., Linker, N., 2006. Performance guarantees of a greedy algorithm for minimizing a supermodular set function on comatroid", which was published in Volume 171 of the Eur ...

ELSEVIER2021

Safe Zeroth-Order Optimization Using Linear Programs

Giancarlo Ferrari Trecate, Yuning Jiang, Maryam Kamgarpour, Baiwei Guo

To solve unmodeled optimization problems with hard constraints, this paper proposes a novel zeroth-order approach called Safe Zeroth-order Optimization using Linear Programs (SZO-LP). The SZO-LP method solves a linear program in each iteration to find a de ...

New York2023

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

Giancarlo Ferrari Trecate, Luca Furieri, Baiwei Guo, 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