Adaptive Consensus Algorithms for Matrix-Weighted Networks with Parametric Uncertainties

In this paper, we study the consensus problem for agents with parametric uncertainties interacting over matrix-weighted graphs. First, an adaptive matrix-weighted consensus algorithms for single-integrator agents is proposed and then extended to double- and higher-order integrator agents. Second, ad...

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Bibliographic Details
Main Authors: Nguyen, Hieu Minh, Nguyen, Hung Manh, Pham, Viet Hoang, Van Tran, Quoc, Van Nguyen, Chuong, Trinh, Minh Hoang, Ahn, Hyo-Sung
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Adaptive systems
Consensus algorithm
Location awareness
Neural networks
Sufficient conditions
Uncertainty
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Summary:In this paper, we study the consensus problem for agents with parametric uncertainties interacting over matrix-weighted graphs. First, an adaptive matrix-weighted consensus algorithms for single-integrator agents is proposed and then extended to double- and higher-order integrator agents. Second, adaptive model-reference adaptive matrix-weighted consensus and its robustness are discussed. For each proposed consensus algorithm, conditions for the adaptive variables to converge to the uncertain parameters are also given. Finally, applications of the proposed consensus algorithms in displacement-based network localization and formation control are discussed and demonstrated by simulations.
ISSN:2475-7896
DOI:10.1109/ICCAIS56082.2022.9990537