131 research outputs found
Event-triggered Consensus for Multi-agent Systems with Asymmetric and Reducible Topologies
This paper studies the consensus problem of multi-agent systems with
asymmetric and reducible topologies. Centralized event-triggered rules are
provided so as to reduce the frequency of system's updating. The diffusion
coupling feedbacks of each agent are based on the latest observations from its
in-neighbors and the system's next observation time is triggered by a criterion
based on all agents' information. The scenario of continuous monitoring is
first considered, namely all agents' instantaneous states can be observed. It
is proved that if the network topology has a spanning tree, then the
centralized event-triggered coupling strategy can realize consensus for the
multi-agent system. Then the results are extended to discontinuous monitoring,
where the system computes its next triggering time in advance without having to
observe all agents' states continuously. Examples with numerical simulation are
provided to show the effectiveness of the theoretical results
Fast-Convergent Dynamics for Distributed Resource Allocation Over Sparse Time-Varying Networks
In this paper, distributed dynamics are deployed to solve resource allocation
over time-varying multi-agent networks. The state of each agent represents the
amount of resources used/produced at that agent while the total amount of
resources is fixed. The idea is to optimally allocate the resources among the
group of agents by reducing the total cost functions subject to fixed amount of
total resources. The information of each agent is restricted to its own state
and cost function and those of its immediate neighbors. This is motivated by
distributed applications such as in mobile edge-computing, economic dispatch
over smart grids, and multi-agent coverage control. The non-Lipschitz dynamics
proposed in this work shows fast convergence as compared to the linear and some
nonlinear solutions in the literature. Further, the multi-agent network
connectivity is more relaxed in this paper. To be more specific, the proposed
dynamics even reaches optimal solution over time-varying disconnected
undirected networks as far as the union of these networks over some bounded
non-overlapping time-intervals includes a spanning-tree. The proposed
convergence analysis can be applied for similar 1st-order resource allocation
nonlinear dynamics. We provide simulations to verify our results
Distributed Optimization for Second-Order Multi-Agent Systems with Dynamic Event-Triggered Communication
In this paper, we propose a fully distributed algorithm for second-order
continuous-time multi-agent systems to solve the distributed optimization
problem. The global objective function is a sum of private cost functions
associated with the individual agents and the interaction between agents is
described by a weighted undirected graph. We show the exponential convergence
of the proposed algorithm if the underlying graph is connected, each private
cost function is locally gradient-Lipschitz-continuous, and the global
objective function is restricted strongly convex with respect to the global
minimizer. Moreover, to reduce the overall need of communication, we then
propose a dynamic event-triggered communication mechanism that is free of Zeno
behavior. It is shown that the exponential convergence is achieved if the
private cost functions are also globally gradient-Lipschitz-continuous.
Numerical simulations are provided to illustrate the effectiveness of the
theoretical results
Solving specified-time distributed optimization problem via sampled-data-based algorithm
Despite significant advances on distributed continuous-time optimization of
multi-agent networks, there is still lack of an efficient algorithm to achieve
the goal of distributed optimization at a pre-specified time. Herein, we design
a specified-time distributed optimization algorithm for connected agents with
directed topologies to collectively minimize the sum of individual objective
functions subject to an equality constraint. With the designed algorithm, the
settling time of distributed optimization can be exactly predefined. The
specified selection of such a settling time is independent of not only the
initial conditions of agents, but also the algorithm parameters and the
communication topologies. Furthermore, the proposed algorithm can realize
specified-time optimization by exchanging information among neighbours only at
discrete sampling instants and thus reduces the communication burden. In
addition, the equality constraint is always satisfied during the whole process,
which makes the proposed algorithm applicable to online solving distributed
optimization problems such as economic dispatch. For the special case of
undirected communication topologies, a reduced-order algorithm is also
designed. Finally, the effectiveness of the theoretical analysis is justified
by numerical simulations
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