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

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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2021-12, Vol.32 (12), p.5644-5653
Main Authors: Gu, Zhou, Yin, Tingting, Ding, Zhengtao
Format: Article
Language:English
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
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!