Proportional Dynamic Consensus in Open Multi-Agent Systems

Abstract : In this paper we investigate a dynamic consensus problem for an open multi-agent system. Open multi-agent systems are characterized by a time-varying set of agents connected by a network: agents may leave and new agents may join the network at any time, thus the term “open”. The dynamic consensus problem consists in achieving agreement about the time-varying average of a set of reference signals that are assumed to be the agents’ inputs. Dynamic consensus has recently found application in the context of distributed estimation for electric demand-side management, where a large population of connected domestic appliances needs to estimate its future average power consumption. Since the considered network of devices changes as new appliances log in and out, there is a need to develop and characterize dynamic consensus algorithms for these open scenarios. In this paper we give several initial contributions both to a general theory of open multi-agent systems and to the specific problem of dynamic consensus within this context. On the theoretical side, we propose a formal definition of open multi-agent system, a suitable notion of stability, and some sufficient conditions to establish it. On the applied side, we design a novel dynamic consensus algorithm, the Open Proportional Dynamic Consensus algorithm. We characterize some of its convergence properties in the proposed open-multi-agent systems framework and we illustrate its evolution by numerical simulations.
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Communication dans un congrès
CDC 2018 - 57th IEEE Conference on Decision and Control, Dec 2018, Miami, United States. pp.1-6, 2018
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https://hal.archives-ouvertes.fr/hal-01945840
Contributeur : Paolo Frasca <>
Soumis le : mercredi 5 décembre 2018 - 15:36:53
Dernière modification le : jeudi 7 février 2019 - 16:56:22

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  • HAL Id : hal-01945840, version 1

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Mauro Franceschelli, Paolo Frasca. Proportional Dynamic Consensus in Open Multi-Agent Systems. CDC 2018 - 57th IEEE Conference on Decision and Control, Dec 2018, Miami, United States. pp.1-6, 2018. 〈hal-01945840〉

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