Adaptive event‐triggered lateral control for autonomous vehicle system under stochastic‐sampling subject to dynamic quantization

Summary In this paper, we delve into the intricate problem of lateral control in autonomous vehicles, utilizing adaptive event triggering, dynamic quantizers, and incorporating stochastic sampling. By integrating the Adaptive Event‐Triggering Scheme (AETS) and dynamic quantizer in dual channels—namely the sensor‐to‐controller and controller‐to‐observer channels—we aptly cater to the multifaceted road conditions faced by autonomous vehicles. Moreover, in light of Denial of Service (DoS) attacks, our controllers ensure system stability amidst stochastic sampling. While ensuring an effective reduction in the amount of network communication data, the efficiency of the output feedback controllers is also significantly improved, thus enabling the closed‐loop system to be strictly dissipative performance stabilized. To substantiate the efficacy of our proposed method, simulation experiments were rigorously conducted using the Carsim‐Simulink platform, highlighting the enhanced safety of autonomous vehicles in real‐world operations.

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Publication Details

Journal
International Journal of Robust and Nonlinear Control
Published
2024-08-20
DOI
https://doi.org/10.1002/rnc.7599
Citations
1
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
0.33

Funders

Controls
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article

Adaptive event‐triggered lateral control for autonomous vehicle system under stochastic‐sampling subject to dynamic quantization

Kaibo Shi, Iyad Katib, Mengzhuo Luo, Jun Cheng et al.
1 citations
International Journal of Robust and Nonlinear Control
Fault Detection and Control Systems
0.33
article

Adaptive event‐triggered lateral control for autonomous vehicle system under stochastic‐sampling subject to dynamic quantization

Kaibo Shi, Iyad Katib, Mengzhuo Luo, Jun Cheng, Wenke Jiang
article en
1 citations

Abstract

Summary In this paper, we delve into the intricate problem of lateral control in autonomous vehicles, utilizing adaptive event triggering, dynamic quantizers, and incorporating stochastic sampling. By integrating the Adaptive Event‐Triggering Scheme (AETS) and dynamic quantizer in dual channels—namely the sensor‐to‐controller and controller‐to‐observer channels—we aptly cater to the multifaceted road conditions faced by autonomous vehicles. Moreover, in light of Denial of Service (DoS) attacks, our controllers ensure system stability amidst stochastic sampling. While ensuring an effective reduction in the amount of network communication data, the efficiency of the output feedback controllers is also significantly improved, thus enabling the closed‐loop system to be strictly dissipative performance stabilized. To substantiate the efficacy of our proposed method, simulation experiments were rigorously conducted using the Carsim‐Simulink platform, highlighting the enhanced safety of autonomous vehicles in real‐world operations.

International Journal of Robust and Nonlinear ControlVol. 34(17)
King Abdulaziz University (SA), Chengdu University of Information Technology (CN), Guangxi Normal University (CN), Guilin University of Aerospace Technology (CN), Guilin University of Technology (CN), Chengdu University (CN), Guilin University of Electronic Technology (CN)
Natural Science Foundation of Guangxi Zhuang Autonomous Region, National Natural Science Foundation of China, Guangxi Normal University
Openalex Percentile: Top 43%
Fault Detection and Control Systems
0.33
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