38,035 research outputs found
Algorithms for Graph-Constrained Coalition Formation in the Real World
Coalition formation typically involves the coming together of multiple,
heterogeneous, agents to achieve both their individual and collective goals. In
this paper, we focus on a special case of coalition formation known as
Graph-Constrained Coalition Formation (GCCF) whereby a network connecting the
agents constrains the formation of coalitions. We focus on this type of problem
given that in many real-world applications, agents may be connected by a
communication network or only trust certain peers in their social network. We
propose a novel representation of this problem based on the concept of edge
contraction, which allows us to model the search space induced by the GCCF
problem as a rooted tree. Then, we propose an anytime solution algorithm
(CFSS), which is particularly efficient when applied to a general class of
characteristic functions called functions. Moreover, we show how CFSS can
be efficiently parallelised to solve GCCF using a non-redundant partition of
the search space. We benchmark CFSS on both synthetic and realistic scenarios,
using a real-world dataset consisting of the energy consumption of a large
number of households in the UK. Our results show that, in the best case, the
serial version of CFSS is 4 orders of magnitude faster than the state of the
art, while the parallel version is 9.44 times faster than the serial version on
a 12-core machine. Moreover, CFSS is the first approach to provide anytime
approximate solutions with quality guarantees for very large systems of agents
(i.e., with more than 2700 agents).Comment: Accepted for publication, cite as "in press
H2 Optimal Coordination of Homogeneous Agents Subject to Limited Information Exchange
Controllers with a diagonal-plus-low-rank structure constitute a scalable
class of controllers for multi-agent systems. Previous research has shown that
diagonal-plus-low-rank control laws appear as the optimal solution to a class
of multi-agent H2 coordination problems, which arise in the control of wind
farms. In this paper we show that this result extends to the case where the
information exchange between agents is subject to limitations. We also show
that the computational effort required to obtain the optimal controller is
independent of the number of agents and provide analytical expressions that
quantify the usefulness of information exchange
A Sub-optimal Algorithm to Synthesize Control Laws for a Network of Dynamic Agents
We study the synthesis problem of an LQR controller when the matrix describing the control law is constrained to lie in a particular vector space. Our motivation is the use of such control laws to stabilize networks of autonomous agents in a decentralized fashion; with the information flow being dictated by the constraints of a pre-specified topology. In this paper, we consider the finite-horizon version of the problem and provide both a computationally intensive optimal solution and a sub-optimal solution that is computationally more tractable. Then we apply the technique to the decentralized vehicle formation control problem and show that the loss in performance due to the use of the sub-optimal solution is not huge; however the topology can have a large effect on performance
A Passivity-Based Distributed Reference Governor for Constrained Robotic Networks
This paper focuses on a passivity-based distributed reference governor (RG)
applied to a pre-stabilized mobile robotic network. The novelty of this paper
lies in the method used to solve the RG problem, where a passivity-based
distributed optimization scheme is proposed. In particular, the gradient
descent method minimizes the global objective function while the dual ascent
method maximizes the Hamiltonian. To make the agents converge to the agreed
optimal solution, a proportional-integral consensus estimator is used. This
paper proves the convergence of the state estimates of the RG to the optimal
solution through passivity arguments, considering the physical system static.
Then, the effectiveness of the scheme considering the dynamics of the physical
system is demonstrated through simulations and experiments.Comment: 8 pages, International Federation of Automatic Conference 2017, 8
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Distributed formation control of multiple unmanned aerial vehicles over time-varying graphs using population games
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a control technique based on distributed population dynamics under time-varying communication graphs for a multi-agent system structured in a leader-follower fashion. Here, the leader agent follows a particular trajectory and the follower agents should track it in a certain organized formation manner. The tracking of the leader can be performed in the position coordinates x; y; and z, and in the yaw angle phi. Additional features are performed with this method: each agent has only partial knowledge of the position of other agents and not necessarily all agents should communicate to the leader. Moreover, it is possible to integrate a new agent into the formation (or for an agent to leave the formation task) in a dynamical manner. In addition, the formation configuration can be changed along the time, and the distributed population-games-based controller achieves the new organization goal accommodating conveniently the information-sharing graph in function of the communication range capabilities of each UAV. Finally, several simulations are presented to illustrate different scenarios, e.g., formation with time-varying communication network, and time-varying formationPeer ReviewedPostprint (author's final draft
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