Dynamic Event‐Triggered Distributed Formation Control for Harbor Unmanned Container Transporter Platoon Under Dos Attacks

ABSTRACT This paper delves into the issue of neuroadaptive distributed event‐triggered resilient formation control for harbor unmanned container transporter (HUCT) platoon in the presence of denial‐of‐service (DoS) attacks and input saturation. The nonlinear dynamics of the vehicle model are approximated by radial basis function neural networks (RBFNNs), while the neural network (NN) state observer is employed to estimate the unmeasurable states of the vehicles. To eliminate the adverse effects of asymmetric input saturation, an improved auxiliary system that incorporates information exchange is developed. Additionally, a distributed resilient formation observer is introduced to estimate the leader's unknown state information when disrupted by DoS attacks. Moreover, a dynamic event‐triggering mechanism (ETM) is established to reduce the update frequency of the controller and conserve limited communication resources. Consequently, a novel distributed observer‐based event‐triggered resilient formation control scheme is devised using the backstepping control technique. It is shown that the proposed control approach can ensure both individual and string stabilities within the HUCT platoon. Ultimately, simulations conducted with two different communication topologies validate the effectiveness of the designed distributed event‐triggered resilient formation controller.

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Publication Details

Journal
International Journal of Robust and Nonlinear Control
Published
2025-09-26
DOI
https://doi.org/10.1002/rnc.70211
Citations
1
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
1.49
Controls
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article

Dynamic Event‐Triggered Distributed Formation Control for Harbor Unmanned Container Transporter Platoon Under Dos Attacks

Shuo Ma, Jun Ning, Kewen Li, Yongming Li
1 citations
International Journal of Robust and Nonlinear Control
Distributed Control Multi-Agent Systems
1.49
article

Dynamic Event‐Triggered Distributed Formation Control for Harbor Unmanned Container Transporter Platoon Under Dos Attacks

Shuo Ma, Jun Ning, Kewen Li, Yongming Li
article en
1 citations

Abstract

ABSTRACT This paper delves into the issue of neuroadaptive distributed event‐triggered resilient formation control for harbor unmanned container transporter (HUCT) platoon in the presence of denial‐of‐service (DoS) attacks and input saturation. The nonlinear dynamics of the vehicle model are approximated by radial basis function neural networks (RBFNNs), while the neural network (NN) state observer is employed to estimate the unmeasurable states of the vehicles. To eliminate the adverse effects of asymmetric input saturation, an improved auxiliary system that incorporates information exchange is developed. Additionally, a distributed resilient formation observer is introduced to estimate the leader's unknown state information when disrupted by DoS attacks. Moreover, a dynamic event‐triggering mechanism (ETM) is established to reduce the update frequency of the controller and conserve limited communication resources. Consequently, a novel distributed observer‐based event‐triggered resilient formation control scheme is devised using the backstepping control technique. It is shown that the proposed control approach can ensure both individual and string stabilities within the HUCT platoon. Ultimately, simulations conducted with two different communication topologies validate the effectiveness of the designed distributed event‐triggered resilient formation controller.

International Journal of Robust and Nonlinear ControlVol. 36(3)
Liaoning University of Technology (CN), Dalian Maritime University (CN)
Openalex Percentile: Top 13%
Distributed Control Multi-Agent Systems
1.49
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Dynamic Event‐Triggered Distributed Formation Control for Harbor Unmanned Container Transporter Platoon Under Dos Attacks — Shuo Ma, Jun Ning, et al. · International Journal of Robust and Nonlinear Control (2025) | TGRS Research Map | TGRS