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Title: Adaptive resilient event-triggered control design of autonomous vehicles with an iterative single critic learning framework
Authors: Zhang, Kun
Su, Rong
Zhang, Huaguang
Tian, Yunlin
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Zhang, K., Su, R., Zhang, H. & Tian, Y. (2021). Adaptive resilient event-triggered control design of autonomous vehicles with an iterative single critic learning framework. IEEE Transactions On Neural Networks and Learning Systems, 32(12), 5502-5511.
Project: 2013-T1-002-177
Journal: IEEE Transactions on Neural Networks and Learning Systems 
Abstract: This paper investigates the adaptive resilient event- triggered control for rear wheel drive autonomous (RWDA) vehicles based on an iterative single critic learning framework, which can effectively balance the frequency/changes in adjusting the vehicle’s control during the running process. According to the kinematic equation of RWDA vehicles and desired trajectory, the tracking error system during autonomous driving process is firstly built, where the denial-of-service (DoS) attacking signals are injected in the networked communication and transmission. Combining the event-triggered sampling mechanism and iterative single critic learning framework, a new event-triggered condition is developed for the adaptive resilient control algorithm, and the novel utility function design are considered for driving the autonomous vehicle, where the control input can be guaranteed into an applicable saturated bound. Finally, we apply the new adaptive resilient control scheme to a case study of driving the RWDA vehicles, and the simulation results illustrate the effectiveness and practicality successfully.
ISSN: 2162-237X
DOI: 10.1109/TNNLS.2021.3053269
Schools: School of Electrical and Electronic Engineering 
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
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