文章基本信息

标题：Neuro–Adaptive Cooperative Control for High–Order Nonlinear Multi–Agent Systems with Uncertainties
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作者：Peng Cheng ; Peng Cheng ; Zhang Anguo 等
期刊名称：International Journal of Applied Mathematics and Computer Science
电子版ISSN：2083-8492
出版年度：2021
卷号：31
期号：4
页码：635-645
DOI：10.34768/amcs-2021-0044
语种：English
出版社：De Gruyter Open
摘要：The consensus problem for a class of high-order nonlinear multi-agent systems (MASs) with external disturbance and system uncertainty is studied. We design an online-update radial basis function (RBF) neural network based distributed adaptive control protocol, where the sliding model control method is also applied to eliminate the influence of the external disturbance and system uncertainty. System consensus is verified by using the Lyapunov stability theorem, and sufficient conditions for cooperative uniform ultimately boundedness (CUUB) are also derived. Two simulation examples demonstrate the effectiveness of the proposed method for both homogeneous and heterogeneous MASs.
关键词：multi-agent systems;RBF neural network;sliding mode control;cooperative control