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H<sub>∞</sub>Cluster Formation Control of Networked Multiagent Systems With Stochastic Sampling

Lang Ma, Yu‐Long Wang, Qing‐Long Han

2020IEEE Transactions on Cybernetics39 citationsDOI

Abstract

This article is concerned with the <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> cluster formation control of a multiagent system (MAS) with stochastic sampling in network environments. First, based on a directed communication topology with acyclic partition, agents are separated into several clusters. The agents moving in the same cluster are expected to achieve a desired formation collaboratively, while the agents in different clusters have different formation patterns. Second, the external disturbance for each agent is taken into account. The associated <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> cluster formation control, whose performance bound can be obtained by considering both formation information and cluster characteristics, is introduced to measure the disturbance attenuation ability. Third, by casting a stochastic sampled-data-based <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> cluster formation problem into an <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> control problem of a stochastic system, an <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> cluster formation criterion is derived for the MAS with external disturbance. Finally, a lower-dimensional cluster formation criterion is obtained for the disturbance-free MAS. Cluster formation performance analysis testifies the effectiveness of the proposed design methods.

Topics & Concepts

Cluster (spacecraft)Partition (number theory)Disturbance (geology)Multi-agent systemComputer scienceControl (management)AttenuationTopology (electrical circuits)Sampling (signal processing)Distributed computingControl theory (sociology)MathematicsCombinatoricsArtificial intelligencePhysicsComputer networkTelecommunicationsPaleontologyOpticsDetectorBiologyDistributed Control Multi-Agent SystemsNeural Networks Stability and SynchronizationMathematical and Theoretical Epidemiology and Ecology Models
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