Distributed Consensus in Networks of Dynamic Agents

Bauso, Dario; Giarré, L.; Pesenti, Raffaele

doi:10.1109/cdc.2005.1583298

Cited by 17 publications

(22 citation statements)

References 23 publications

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“…Under the conditions in Theorem 4.2, consensus protocol (12) guarantees that x ! 1p T xð0Þ þ ð1=aÞ1p T zð0Þ and z !…”

Section: Theorem 43mentioning

confidence: 97%

“…We also have the following sufficient condition for information consensus using consensus protocol (12).…”

Section: Proof (Sufficiency)mentioning

confidence: 99%

“…Without loss of generality, we let m 1 ¼ 0; which implies that r 1þ ¼ 0 and r 1À ¼ Àa: We have the following lemma regarding a necessary and sufficient condition for information consensus using consensus protocol (12).…”

Section: Lemma 45mentioning

confidence: 99%

“…Let p be a non-negative left eigenvector of ÀL associated with eigenvalue 0 and p T 1 ¼ 1: With consensus protocol (12), xðtÞ ! 1p T xð0Þ þ ð1=aÞ1p T zð0Þ and zðtÞ !…”

Section: Lemma 46mentioning

confidence: 99%

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Distributed multi‐vehicle coordinated control via local information exchange

Ren

Atkins

2006

Intl J Robust & Nonlinear

1,317

727

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SUMMARYThis paper describes a distributed coordination scheme with local information exchange for multiple vehicle systems. We introduce second-order consensus protocols that take into account motions of the information states and their derivatives, extending first-order protocols from the literature. We also derive necessary and sufficient conditions under which consensus can be reached in the context of unidirectional information exchange topologies. This work takes into account the general case where information flow may be unidirectional due to sensors with limited fields of view or vehicles with directed, power-constrained communication links. Unlike the first-order case, we show that having a (directed) spanning tree is a necessary rather than a sufficient condition for consensus seeking with second-order dynamics. This work focuses on a formal analysis of information exchange topologies that permit second-order consensus. Given its importance to the stability of the coordinated system, an analysis of the consensus term control gains is also presented, specifically the strength of the information states relative to their derivatives. As an illustrative example, consensus protocols are applied to coordinate the movements of multiple mobile robots.

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“…Under the conditions in Theorem 4.2, consensus protocol (12) guarantees that x ! 1p T xð0Þ þ ð1=aÞ1p T zð0Þ and z !…”

Section: Theorem 43mentioning

confidence: 97%

“…We also have the following sufficient condition for information consensus using consensus protocol (12).…”

Section: Proof (Sufficiency)mentioning

confidence: 99%

Section: Lemma 45mentioning

confidence: 99%

“…Let p be a non-negative left eigenvector of ÀL associated with eigenvalue 0 and p T 1 ¼ 1: With consensus protocol (12), xðtÞ ! 1p T xð0Þ þ ð1=aÞ1p T zð0Þ and zðtÞ !…”

Section: Lemma 46mentioning

confidence: 99%

See 2 more Smart Citations

Distributed multi‐vehicle coordinated control via local information exchange

Ren

Atkins

2006

Intl J Robust & Nonlinear

1,317

727

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“…Suppose that vehicle k is the unique team leader. The control law for vehicle k is designed as (10) where K rk and K vk are m × m symmetric positive definite matrices. The control law for follower vehicle i is designed as…”

Section: B Leader-follower Strategymentioning

confidence: 99%

Second-order Consensus Algorithm with Extensions to Switching Topologies and Reference Models

Ren

2007

2007 American Control Conference

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In this paper, we extend the consensus algorithm for double integrator dynamics to the case that the information exchange topologies switch randomly with time and to the case that the final consensus value evolves according to a given nonlinear reference model. We show sufficient conditions under which consensus is reached under switching directed information exchange topologies. Unlike the consensus algorithm for single integrator dynamics, more stringent conditions are required to guarantee consensus under switching directed topologies in the case of the consensus algorithm for double integrator dynamics. In addition, we propose consensus algorithms so that the information variables of each vehicle approach the solution of a nonlinear reference model when only a portion of the vehicles in the team have access to the model.

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Interaction network effects on position- and velocity-based models of collective motion

Turgut

Boz

Okay

et al. 2020

J. R. Soc. Interface.

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We study how the structure of the interaction network affects self-organized collective motion in two minimal models of self-propelled agents: the Vicsek model and the Active-Elastic (AE) model. We perform simulations with topologies that interpolate between a nearest-neighbour network and random networks with different degree distributions to analyse the relationship between the interaction topology and the resilience to noise of the ordered state. For the Vicsek case, we find that a higher fraction of random connections with homogeneous or power-law degree distribution increases the critical noise, and thus the resilience to noise, as expected due to small-world effects. Surprisingly, for the AE model, a higher fraction of random links with power-law degree distribution can decrease this resilience, despite most links being long-range. We explain this effect through a simple mechanical analogy, arguing that the larger presence of agents with few connections contributes localized low-energy modes that are easily excited by noise, thus hindering the collective dynamics. These results demonstrate the strong effects of the interaction topology on self-organization. Our work suggests potential roles of the interaction network structure in biological collective behaviour and could also help improve decentralized swarm robotics control and other distributed consensus systems.

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Distributed Consensus in Networks of Dynamic Agents

Cited by 17 publications

References 23 publications

Distributed multi‐vehicle coordinated control via local information exchange

Distributed multi‐vehicle coordinated control via local information exchange

Second-order Consensus Algorithm with Extensions to Switching Topologies and Reference Models

Interaction network effects on position- and velocity-based models of collective motion

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