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This paper considers the stochastic consensus problem of high-order linear discrete-time multiagent systems (MASs) with uncertain topologies and external disturbances. The randomly switching interaction signed topologies are governed by a time-homogenous Markov chain with incomplete knowledge of the transition probabilities. Based on Lyapunov function theory and adaptive control strategies, we employ a homogeneous parameter-dependent Lyapunov function to propose sufficient conditions under a set of linear matrix inequality (LMI) constraints. A free-weighting matrix method is introduced to give the proposed LMIs extra freedom to search feasible solutions. Furthermore, the adaptive controllers applied in this paper are independent of topologies and fully distributed; i.e., the system achieves robust consensus without continuously updating the controller and dealing with global data. Additionally, we extend the theoretical framework to analyze the disturbance rejection performance of MASs under the stochastic process. Two numerical examples are provided to show the effectiveness of the algorithms. (c) 2021 Elsevier B.V. All rights reserved.
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NEUROCOMPUTING
ISSN: 0925-2312
Year: 2021
Volume: 449
Page: 100-107
5 . 7 7 9
JCR@2021
5 . 5 0 0
JCR@2023
ESI Discipline: COMPUTER SCIENCE;
ESI HC Threshold:106
JCR Journal Grade:2
CAS Journal Grade:3
Cited Count:
WoS CC Cited Count: 9
SCOPUS Cited Count: 9
ESI Highly Cited Papers on the List: 0 Unfold All
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30 Days PV: 0
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