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Type: Journal article
Title: Adaptive neural control for a class of nonlinear multiagent systems
Author: Zheng, S.
Shi, P.
Wang, S.
Shi, Y.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2021; 32(2):763-776
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2021
ISSN: 2162-237X
Statement of
Shiqi Zheng, Peng Shi, Shuoyu Wang and Yan Shi
Abstract: This article studies the adaptive neural controller design for a class of uncertain multiagent systems described by ordinary differential equations (ODEs) and beams. Three kinds of agent models are considered in this study, i.e., beams, nonlinear ODEs, and coupled ODE and beams. Both beams and ODEs contain completely unknown nonlinearities. Moreover, the control signals are assumed to suffer from a class of generalized backlash nonlinearities. First, neural networks (NNs) are adopted to approximate the completely unknown nonlinearities. New barrier Lyapunov functions are constructed to guarantee the compact set conditions of the NNs. Second, new adaptive neural proportional integral (PI)-type controllers are proposed for the networked ODEs and beams. The parameters of the PI controllers are adaptively tuned by NNs, which can make the system output remain in a prescribed time-varying constraint. Two illustrative examples are presented to demonstrate the advantages of the obtained results.
Keywords: Adaptive proportional integral (PI) controller; barrier Lyapunov function; beams; multiagent systems; neural networks (NNs)
Rights: © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.
DOI: 10.1109/TNNLS.2020.2979266
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Appears in Collections:Aurora harvest 4
Electrical and Electronic Engineering publications

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