<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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...

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

Online veröffentlicht: 30. Dez. 2021

Seitenbereich: 635 - 645

Eingereicht: 14. Juni 2021

Akzeptiert: 05. Aug. 2021

DOI: https://doi.org/10.34768/amcs-2021-0044

Schlüsselwörter
multi-agent systems, RBF neural network, sliding mode control, cooperative control

© 2021 Cheng Peng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

Online veröffentlicht: 30. Dez. 2021

Seitenbereich: 635 - 645

Eingereicht: 14. Juni 2021

Akzeptiert: 05. Aug. 2021

DOI: https://doi.org/10.34768/amcs-2021-0044

Schlüsselwörtermulti-agent systems, RBF neural network, sliding mode control, cooperative control

© 2021 Cheng Peng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
multi-agent systems, RBF neural network, sliding mode control, cooperative control