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A Dynamic Gain Fixed-Time Robust ZNN Model for Time-Variant Equality Constrained Quaternion Least Squares Problem With Applications to Multiagent Systems

Lookup NU author(s): Dr Jichun Li

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

IEEEA dynamic gain fixed-time (FXT) robust zeroing neural network (DFTRZNN) model is proposed to effectively solve time-variant equality constrained quaternion least squares problem (TV-EQLS). The proposed approach surmounts the shortcomings of conventional numerical algorithms which fail to address time-variant problems. The DFTRZNN model is constructed with a novel dynamic gain parameter and a novel activation function (NAF), which differs from previous zeroing neural network (ZNN) models. Moreover, the comprehensive theoretical derivation of the FXT stability and robustness of the DFTRZNN model is presented in detail. Simulation results further confirm the availability and superiority of the DFTRZNN model for solving TV-EQLS. Finally, the consensus protocols of multiagent systems are presented by utilizing the design scheme of the DFTRZNN model, which further demonstrates its practical application value.

Publication metadata

Author(s): Cao P, Xiao L, He Y, Li J

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Neural Networks and Learning Systems

Year: 2023

Pages: epub ahead of print

Online publication date: 06/10/2023

Acceptance date: 10/09/2023

Date deposited: 23/11/2023

ISSN (print): 2162-237X

ISSN (electronic): 2162-2388

Publisher: IEEE

URL: https://doi.org/10.1109/TNNLS.2023.3315332

DOI: 10.1109/TNNLS.2023.3315332

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