Resilient neural network-based control of nonlinear heterogeneous multi-agent systems: a cyber-physical system approach-Reference-Cited by-同舟云学术

Resilient neural network-based control of nonlinear heterogeneous multi-agent systems: a cyber-physical system approach

Published:2023-08-27 Issue:20 Volume:111 Page:19171-19185
ISSN:0924-090X
Container-title:Nonlinear Dynamics
language:en
Short-container-title:Nonlinear Dyn

Author:

Khoshnevisan Ladan,Liu Xinzhi^ORCID

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Applied Mathematics,Mechanical Engineering,Ocean Engineering,Aerospace Engineering,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11071-023-08840-w.pdf

Reference27 articles.

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3. Guo, H., Liu, J., Dai, Q., Chen, H., Wang, Y., Zhao, W.: A distributed adaptive triple-step nonlinear control for a connected automated vehicle platoon with dynamic uncertainty. IEEE Internet Things J. 7(5), 3861–3871 (2020). https://doi.org/10.1109/JIOT.2020.2973977

4. Wang, Q., Jin, S., Hou, Z.: Data-driven event-triggered cooperative control for multiple subway trains with switching topologies. IEEE Trans. Intell. Transp. Syst. (2021). https://doi.org/10.1109/TITS.2021.3131997

5. Wang, Y., Bian, N., Zhang, L., Huang, Y., Chen, H.: Resilient path-following control of autonomous vehicles subject to intermittent denial-of-service attacks. IET Intel. Transp. Syst. 15(12), 1508–1521 (2021). https://doi.org/10.1049/itr2.12114

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