Zobrazeno 1 - 10
of 118
pro vyhledávání: '"Paré, Philip. E."'
We consider a susceptible-infected-susceptible (SIS) epidemic model in which a large group of individuals decide whether to adopt partially effective protection without being aware of their individual infection status. Each individual receives a sign
Externí odkaz:
http://arxiv.org/abs/2410.20303
Autor:
Butler, Brooks A., Paré, Philip E.
This letter explores the implementation of a safe control law for systems of dynamically coupled cooperating agents. Under a CBF-based collaborative safety framework, we examine how the maximum safety capability for a given agent, which is computed u
Externí odkaz:
http://arxiv.org/abs/2410.16280
In this paper, we develop a framework for the automatic taxiing of aircraft between hangar and take-off given a graph-based model of an airport. We implement a high-level path-planning algorithm that models taxiway intersections as nodes in an undire
Externí odkaz:
http://arxiv.org/abs/2410.03890
This work explores a collaborative method for ensuring safety in multi-agent formation control problems. We formulate a control barrier function (CBF) based safety filter control law for a generic distributed formation controller and extend our previ
Externí odkaz:
http://arxiv.org/abs/2410.03885
Global and Distributed Reproduction Numbers of a Multilayer SIR Model with an Infrastructure Network
In this paper, we propose an SIR spread model in a population network coupled with an infrastructure network that has a pathogen spreading in it. We develop a threshold condition to characterize the monotonicity and peak time of a weighted average of
Externí odkaz:
http://arxiv.org/abs/2409.08430
We study the impact of parameter estimation and state measurement errors on a control framework for optimally mitigating the spread of epidemics. We capture the epidemic spreading process using a susceptible-infected-removed (SIR) epidemic model and
Externí odkaz:
http://arxiv.org/abs/2408.03447
In this work, we first show that the problem of parameter identification is often ill-conditioned and lacks the persistence of excitation required for the convergence of online learning schemes. To tackle these challenges, we introduce the notion of
Externí odkaz:
http://arxiv.org/abs/2406.10349
Modeling epidemic spread is critical for informing policy decisions aimed at mitigation. Accordingly, in this work we present a new data-driven method based on Gaussian process regression (GPR) to model epidemic spread. We bound the variance of the p
Externí odkaz:
http://arxiv.org/abs/2312.09384
The safe control of multi-robot swarms is a challenging and active field of research, where common goals include maintaining group cohesion while simultaneously avoiding obstacles and inter-agent collision. Building off our previously developed theor
Externí odkaz:
http://arxiv.org/abs/2311.11156
Effective containment of spreading processes such as epidemics requires accurate knowledge of several key parameters that govern their dynamics. In this work, we first show that the problem of identifying the underlying parameters of epidemiological
Externí odkaz:
http://arxiv.org/abs/2311.01337