Logical consensus for distributed network agreement

Fagiolini, Adriano; Visibelli, E.M.; Bicchi, Antonio

doi:10.1109/cdc.2008.4738964

Cited by 13 publications

(20 citation statements)

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“…The proposed solutions range from using gossip-type algorithms (where an agent randomly contacts a neighbor and then they bring their values as close together as possible) [11], to incorporating quantization steps into (otherwise) linear update strategies for each agent [10]. Along similar lines, the topic of logical consensus (where agents are expected to reach agreement on certain Boolean functions of various Boolean inputs) has been studied in [13].…”

Section: Introductionmentioning

confidence: 99%

Control of quantized multi-agent systems with linear nearest neighbor rules: A finite field approach

Sundaram

Hadjicostis

2010

Proceedings of the 2010 American Control Conference

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We study the problem of controlling a multi-agent system where each agent is only allowed to be in a discrete and finite set of states. Each agent is capable of updating its state based on the states of its neighbors, and there is a leader agent in the network that is allowed to update its state in arbitrary ways (within the discrete set) in order to put all agents in a desired state. We present a novel solution to this problem by viewing the discrete states of the system as elements of a finite field. Specifically, we develop a theory of structured linear systems over finite fields, and show that such systems will be controllable provided that the size of the finite field is sufficiently large, and that the graph associated with the system satisfies certain properties. We then use these results to show that a multi-agent system with a leader node is controllable via a linear nearest-neighbor update as long as there is a path from the leader to every node, and that the number of discrete states for each node is large enough. ©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/grasp_papers/55 Control of Quantized Multi-Agent Systems with Linear NearestNeighbor Rules: A Finite Field Approach Shreyas Sundaram and Christoforos N. HadjicostisAbstract-We study the problem of controlling a multi-agent system where each agent is only allowed to be in a discrete and finite set of states. Each agent is capable of updating its state based on the states of its neighbors, and there is a leader agent in the network that is allowed to update its state in arbitrary ways (within the discrete set) in order to put all agents in a desired state. We present a novel solution to this problem by viewing the discrete states of the system as elements of a finite field. Specifically, we develop a theory of structured linear systems over finite fields, and show that such systems will be controllable provided that the size of the finite field is sufficiently large, and that the graph associated with the system satisfies certain properties. We then use these results to show that a multi-agent system with a leader node is controllable via a linear nearest-neighbor update as long as there is a path from the leader to every node, and that the number of discrete states for each node is large enough.

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Section: Introductionmentioning

confidence: 99%

Control of quantized multi-agent systems with linear nearest neighbor rules: A finite field approach

Sundaram

Hadjicostis

2010

Proceedings of the 2010 American Control Conference

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“…Swarms are made up of either social [29,61,78,77], biological [90,79,110,111,78,6], robotic [44,72,114,47,63] or software individuals as in peer-to-peer networks [2]. A closely related field of research comprises sensor networks [99,20,100,57], and consensus problems [75,76,87,74,65,73,92,28,41,101]. In sensor networks, thanks to the existence of many and possibly inexpensive sensing nodes, a lot of measurements of the same group of variables are available, for instance the temperature distribution in a given environment.…”

Section: Introductionmentioning

confidence: 99%

Distributed Fault Detection and Isolation of Continuous-Time Non-Linear Systems

Boem

Ferrari

Parisini

2011

European Journal of Control

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“…Logical consensus is considered in [16] for the purpose of intruder and event detection. In logical consensus agents aim to coordinate their decisions via distributed computation of a function of a set of logical (boolean) events.…”

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confidence: 99%

Finite-field consensus

Pasqualetti

Borra

Bullo

2013

52nd IEEE Conference on Decision and Control

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This work studies consensus networks over finite fields, where agents process and communicate values from the set of integers {0, . . . , p − 1}, for some prime number p, and operations are performed modulo p. For consensus networks over finite fields we provide necessary and sufficient conditions on the network topology and weights to ensure convergence. For instance we show that, differently from the case of consensus networks over the field of real numbers, consensus networks over finite fields converge in finite time, and that properties of the agents interaction graph are not sufficient to ensure finitefield consensus. Finally, we discuss the application of finite-field consensus to distributed averaging in sensor networks.

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Logical consensus for distributed network agreement

Cited by 13 publications

References 10 publications

Control of quantized multi-agent systems with linear nearest neighbor rules: A finite field approach

Control of quantized multi-agent systems with linear nearest neighbor rules: A finite field approach

Distributed Fault Detection and Isolation of Continuous-Time Non-Linear Systems

Finite-field consensus

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