2,439 research outputs found
Output consensus of nonlinear multi-agent systems with unknown control directions
In this paper, we consider an output consensus problem for a general class of
nonlinear multi-agent systems without a prior knowledge of the agents' control
directions. Two distributed Nussbaumtype control laws are proposed to solve the
leaderless and leader-following adaptive consensus for heterogeneous multiple
agents. Examples and simulations are given to verify their effectivenessComment: 10 pages;2 figure
Persistence based analysis of consensus protocols for dynamic graph networks
This article deals with the consensus problem involving agents with
time-varying singularities in the dynamics or communication in undirected graph
networks. Existing results provide control laws which guarantee asymptotic
consensus. These results are based on the analysis of a system switching
between piecewise constant and time-invariant dynamics. This work introduces a
new analysis technique relying upon classical notions of persistence of
excitation to study the convergence properties of the time-varying multi-agent
dynamics. Since the individual edge weights pass through singularities and vary
with time, the closed-loop dynamics consists of a non-autonomous linear system.
Instead of simplifying to a piecewise continuous switched system as in
literature, smooth variations in edge weights are allowed, albeit assuming an
underlying persistence condition which characterizes sufficient inter-agent
communication to reach consensus. The consensus task is converted to
edge-agreement in order to study a stabilization problem to which classical
persistence based results apply. The new technique allows precise computation
of the rate of convergence to the consensus value.Comment: This article contains 7 pages and includes 4 figures. it is accepted
in 13th European Control Conferenc
Cloud-Based Optimization: A Quasi-Decentralized Approach to Multi-Agent Coordination
New architectures and algorithms are needed to reflect the mixture of local
and global information that is available as multi-agent systems connect over
the cloud. We present a novel architecture for multi-agent coordination where
the cloud is assumed to be able to gather information from all agents, perform
centralized computations, and disseminate the results in an intermittent
manner. This architecture is used to solve a multi-agent optimization problem
in which each agent has a local objective function unknown to the other agents
and in which the agents are collectively subject to global inequality
constraints. Leveraging the cloud, a dual problem is formulated and solved by
finding a saddle point of the associated Lagrangian.Comment: 7 pages, 3 figure
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