Creator:

Peng, Cheng ; Zhang, Anguo ; Li, Junyu

Contributor:

Kusy, Maciej - ed. ; Scherer, Rafał - ed. ; Krzyżak, Adam - ed.

Title:

Neuro-adaptive cooperative control for high-order nonlinear multi-agent systems with uncertainties

Subtitle:

.

Group publication title:

AMCS, volume 31 (2021)

Subject and Keywords:

multi-agent systems ; RBF neural network ; sliding mode control ; cooperative control

Abstract:

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.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Date:

2021

Resource Type:

artykuł

DOI:

10.34768/amcs-2021-0044

Pages:

635-645

Source:

AMCS, volume 31, number 4 (2021) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego