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
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2021.3056764

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 learning-based event-triggered mechanism (ETM) is proposed. Different from existing ETMs, the triggering threshold of the proposed mechanism can be dynamically adjusted with conditions of the latest vehicle state. Each vehicle in this study is deemed as an agent, under which a novel control strategy is developed for these autonomous agents with deception attacks. With the assistance of Lyapunov stability theory, sufficient conditions are obtained to guarantee the stability and stabilization of the overall system. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed theoretical results.