19 research outputs found
Decentralized Abstractions for Feedback Interconnected Multi-Agent Systems
The purpose of this report is to define abstractions for multi-agent systems
under coupled constraints. In the proposed decentralized framework, we specify
a finite or countable transition system for each agent which only takes into
account the discrete positions of its neighbors. The dynamics of the considered
systems consist of two components. An appropriate feedback law which guarantees
that certain performance requirements (eg. connectivity) are preserved and
induces the coupled constraints and additional free inputs which we exploit in
order to accomplish high level tasks. In this work we provide sufficient
conditions on the space and time discretization of the system which ensure that
we can extract a well posed and hence meaningful finite transition system.Comment: 15 page
Robust Connectivity Analysis for Multi-Agent Systems
In this report we provide a decentralized robust control approach, which
guarantees that connectivity of a multi-agent network is maintained when
certain bounded input terms are added to the control strategy. Our main
motivation for this framework is to determine abstractions for multi-agent
systems under coupled constraints which are further exploited for high level
plan generation.Comment: 20 page
Online Abstractions for Interconnected Multi-Agent Control Systems
In this report, we aim at the development of an online abstraction framework
for multi-agent systems under coupled constraints. The motion capabilities of
each agent are abstracted through a finite state transition system in order to
capture reachability properties of the coupled multi-agent system over a finite
time horizon in a decentralized manner. In the first part of this work, we
define online abstractions by discretizing an overapproximation of the agents'
reachable sets over the horizon. Then, sufficient conditions relating the
discretization and the agent's dynamics properties are provided, in order to
quantify the transition possibilities of each agent.Comment: 22 pages. arXiv admin note: text overlap with arXiv:1603.0478
Structured ambiguity sets for distributionally robust optimization
Distributionally robust optimization (DRO) incorporates robustness against
uncertainty in the specification of probabilistic models. This paper focuses on
mitigating the curse of dimensionality in data-driven DRO problems with optimal
transport ambiguity sets. By exploiting independence across lower-dimensional
components of the uncertainty, we construct structured ambiguity sets that
exhibit a faster shrinkage as the number of collected samples increases. This
narrows down the plausible models of the data-generating distribution and
mitigates the conservativeness that the decisions of DRO problems over such
ambiguity sets may face. We establish statistical guarantees for these
structured ambiguity sets and provide dual reformulations of their associated
DRO problems for a wide range of objective functions. The benefits of the
approach are demonstrated in a numerical example