activity
20232026
most citedPerspectives on Contractivity in Control, Optimization, and Learning

8 citations · 17 across the 16 of their papers we have counts for

collaborators
Showing eess.SYShow all

5 papers · 1 filter

eess.SY2026

Deceptive Stochastic Patrolling via Markov Chain Lifting

Yohan John, Gilberto Diaz-Garcia, Jason R. Marden +1

In this paper we propose lifted Markov Chains (MCs) as a new paradigm for deriving patrol strategies for mobile agents on an environment represented as a graph. Lifted MCs operate…

eess.SY2025

Sampled-data Systems: Stability, Contractivity and Single-iteration Suboptimal MPC

Yiting Chen, Francesco Bullo, Emiliano Dall'Anese

This paper analyzes the stability of interconnected continuous-time (CT) and discrete-time (DT) systems coupled through sampling and zero-order hold mechanisms. The DT system updat…

eess.SY2024★ 8 cited

Perspectives on Contractivity in Control, Optimization, and Learning

Alexander Davydov, Francesco Bullo

Contraction theory is a mathematical framework for studying the convergence, robustness, and modularity properties of dynamical systems and algorithms. In this opinion paper, we pr…

eess.SY2023

Multiplayer Homicidal Chauffeur Reach-Avoid Games: A Pursuit Enclosure Function Approach

Rui Yan, Xiaoming Duan, Rui Zou +3

This paper presents a multiplayer Homicidal Chauffeur reach-avoid differential game, which involves Dubins-car pursuers and simple-motion evaders. The goal of the pursuers is to co…

eess.SY2023★ 3 cited

Online Feedback Optimization and Singular Perturbation via Contraction Theory

Liliaokeawawa Cothren, Francesco Bullo, Emiliano Dall'Anese

In this paper, we provide a novel contraction-theoretic approach to analyze two-time scale systems, including those commonly encountered in Online Feedback Optimization (OFO). Our…