Path Tracking Control of Autonomous Vehicles Subject to Deception Attacks via a Learning-Based Event-Triggered Mechanism

• Published in: IEEE transaction on neural networks and learning systems Vol. 32; no. 12; pp. 5644 - 5653
• Main Authors: Gu, Zhou, Yin, Tingting, Ding, Zhengtao
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Autonomous ground vehicles (AGVs); Autonomous vehicles; Axles; Communication networks; Data models; Deception; deception attacks; Denial-of-service attack; Learning; learning-based event-triggered control; Mathematical model; Path tracking; Stability; Symmetric matrices; Tracking control; Unmanned ground vehicles; Vehicles

This article investigates the problem of event-triggered secure path tracking control of autonomous ground vehicles (AGVs) under deception attacks. To relieve the burden of the shareable vehicle communication network and to improve the tracking performance in the presence of deception attacks, a lea...