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Paper details
Number 4 - December 2021
Volume 31 - 2021
Neuro-adaptive cooperative control for high-order nonlinear multi-agent systems with uncertainties
Cheng Peng, Anguo Zhang, Junyu Li
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.
Keywords
multi-agent systems, RBF neural network, sliding mode control, cooperative contro