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

Author:

Jiang Wenke1,Luo Mengzhuo1ORCID,Cheng Jun2ORCID,Katib Iyad3,Shi Kaibo4ORCID

Affiliation:

1. School of Mathematics and Statistics Guilin University of Technology Guilin People's Republic of China

2. School of Mathematics and Statistics Guangxi Normal University Guilin People's Republic of China

3. Department of Computer Science Faculty of Computing and Information Technology, King Abdulaziz University Jeddah Saudi Arabia

4. School of Information Science and Engineering Chengdu University Chengdu People's Republic of China

Abstract

SummaryIn 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.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangxi Zhuang Autonomous Region

Guangxi Normal University

Publisher

Wiley

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