Data-Driven Dynamic Event-Triggered Sliding-Mode Heading Control for Unmanned Surface Vehicles with Uncertainties

This paper investigates a data-driven dynamic event-triggered sliding mode heading control problem for un-manned surface vehicles with uncertain dynamics models. First, a virtual sensor is introduced to establish a compact dynamic linearization model for the unmanned surface vehicle. Then, a dynamic event-triggered scheme is developed to alleviate the communication burden. Moreover, a sliding mode surface is designed, and a data-driven dynamic event-triggered sliding mode heading control approach is formulated. Finally, rigorous mathematical proofs are given, and several simulations demon-strate the effectiveness and superiority of the proposed method compared to existing approaches.

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Publication Details

Published
2024-10-06
DOI
https://doi.org/10.1109/smc54092.2024.10831303
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
0.00

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article

Data-Driven Dynamic Event-Triggered Sliding-Mode Heading Control for Unmanned Surface Vehicles with Uncertainties

Xing Li, Jinjun Shan, Huarong Zhao, Hongnian Yu
Fault Detection and Control Systems
article

Data-Driven Dynamic Event-Triggered Sliding-Mode Heading Control for Unmanned Surface Vehicles with Uncertainties

Xing Li, Jinjun Shan, Huarong Zhao, Hongnian Yu
article en

Abstract

This paper investigates a data-driven dynamic event-triggered sliding mode heading control problem for un-manned surface vehicles with uncertain dynamics models. First, a virtual sensor is introduced to establish a compact dynamic linearization model for the unmanned surface vehicle. Then, a dynamic event-triggered scheme is developed to alleviate the communication burden. Moreover, a sliding mode surface is designed, and a data-driven dynamic event-triggered sliding mode heading control approach is formulated. Finally, rigorous mathematical proofs are given, and several simulations demon-strate the effectiveness and superiority of the proposed method compared to existing approaches.

Jiangnan University (CN), York University (CA), Edinburgh Napier University (GB), Dongguan University of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 69%
Fault Detection and Control Systems
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