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

• Published in: International journal of robust and nonlinear control Vol. 34; no. 17; pp. 11819 - 11844
• Main Authors: Jiang, Wenke, Luo, Mengzhuo, Cheng, Jun, Katib, Iyad, Shi, Kaibo
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 25.11.2024
• Subjects: adaptive event‐triggered scheme; Adaptive sampling; Adaptive systems; Channels; Counters; Denial of service attacks; dynamic quantization; Effectiveness; Feedback control; Lateral control; networked control systems; Output feedback; Road conditions; stochastic sampling; Systems stability; Takagi–Sugeno fuzzy systems; Vehicles
• ISSN: 1049-8923; 1099-1239
• DOI: 10.1002/rnc.7599

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.