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

Peng, Cheng ; Zhang, Anguo ; Li, Junyu

International Journal of Applied Mathematics and Computer Science, Tome 31 (2021) no. 4, pp. 635-645.

Résumé

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.

Détail

Keywords: multiagent system, radial basis function, RBF neural network, sliding mode control, cooperative control
Mots-clés : system wieloagentowy, radialna funkcja bazowa, sieć neuronowa RBF, sterowanie ślizgowe

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