Coordination and Consensus of Networked Agents with Noisy Measurements: Stochastic Algorithms and Asymptotic Behavior

Huang, Minyi; Manton, Jonathan H.

doi:10.1137/06067359x

Cited by 369 publications

(264 citation statements)

References 24 publications

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“…The signal model is extended to vector measurements and to multiple values of the bias in (Fagnani et al, 2014a). Each node uses consensus (Huang and Manton, 2009) to achieve cooperation with the neighbors for the estimation of the constant parameter, while iteratively estimating its own local bias. Consensus algorithms are used also in (Fagnani et al, 2014b), where the two classes depend on the possible values of the noise variance.…”

Section: Related Workmentioning

confidence: 99%

Peer-Assisted Individual Assessment in a multi-agent system

Bassi

Galluccio

et al. 2017

Automatica

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Consider a multi-agent system where agents perform a given task with different levels of ability. Agents are initially not aware of how well they perform in comparison with their peers, and are willing to self-assess. This scenario is relevant, e.g., in wireless sensor networks, or in crowdsensing applications, where devices with embedded sensing capabilities collaboratively collect data to characterize the environment: the global performance is very sensitive to the measurement accuracy, and agents providing outliers should restrain to participate. This paper presents a distributed algorithm enabling each agent to self-assess its own ability. The algorithm tracks the outcomes of a local comparison test performed by pairs of agents when they randomly meet, and able to gauge their relative level of ability. The dynamics of the proportions of agents with similar assessments are described using continuous-time state equations. The existence of an equilibrium is shown. Closed-form expressions for the various proportions of agents with similar assessments are provided at equilibrium. In simulations, a community of agents equipped with sensors, and trying to determine the performance of their equipment is considered. Simulation results show a good fitting with theoretical predictions.

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Section: Related Workmentioning

confidence: 99%

Peer-Assisted Individual Assessment in a multi-agent system

Bassi

Galluccio

et al. 2017

Automatica

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“…For the case that agent k receives noisy measurements of the states of its neighbors, q kl t is the resulting measurement of agent l's states by agent k [4]:…”

Section: A Synchronization On the N-torusmentioning

confidence: 99%

“…We rewrite (9) to adopt the structure of the recursion used in the classic stochastic approximation algorithm [4] …”

Section: A Synchronization On the N-torusmentioning

confidence: 99%

“…Consider the following conditions and assumptions for the additive noise and step size [4]: A stochastic Lyapunov function can be defined based on the state differences of every pair of connected agents averaged over multiple random trials. Let potential P k t for agent k be [4]…”

Section: A Synchronization On the N-torusmentioning

confidence: 99%

“…Robustness of consensus to noise for directed communication characterized by the H 2 norm of the system is studied in [15]. A stochastic adaptive algorithm approach is used in [4], [5], in which a stochastic approximation type algorithm with a decreasing step size is proposed and convergence results are established with measurement noise.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Synchronization on the N-torus with noisy measurements

Mahmoudian

Paley

2011

Proceedings of the 2011 American Control Conference

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Abstract-We consider the problem of cooperative control of unmanned vehicles in an uncertain environment in which each vehicle can obtain only noisy measurements of the other vehicles. In particular, we want to establish convergence to a desired formation using a stochastic algorithm with measurement errors. In this paper, the problem of stabilization of parallel formations in a self-propelled particle model is considered, which can be modeled as synchronization on the N -torus in the presence of noisy measurements of relative phase. Simulations are included to illustrate the result.

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Event‐triggered mean square consensus of linear multiagent systems with measurement noises

2020

Adaptive Control & Signal

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Summary This article investigates mean square consensus of linear multiagent systems with measurement noises based on event‐triggered control. Considering various uncertain factors in the communication environment, multiplicative noises exists in the information that each agent measures from its neighbors. In order to save limited resources and to exclude the Zeno phenomenon, the update of the controller for each agent is driven by properly defined event‐triggered condition. Sufficient conditions are given to ensure exponential mean square consensus of multiagent systems with multiplicative noises by utilizing static and dynamic event‐triggered scheme. The interevent times of static event‐triggered mechanism is less than those of corresponding dynamic case for each agent. Finally, validity of the theoretical results is illustrated through a simulation example.

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Coordination and Consensus of Networked Agents with Noisy Measurements: Stochastic Algorithms and Asymptotic Behavior

Cited by 369 publications

References 24 publications

Peer-Assisted Individual Assessment in a multi-agent system

Peer-Assisted Individual Assessment in a multi-agent system

Synchronization on the N-torus with noisy measurements

Event‐triggered mean square consensus of linear multiagent systems with measurement noises

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