Secure Tracking Control via Fixed-Time Convergent Reinforcement Learning for a UAV CPS

Dear Editor, This letter is concerned with the secure tracking control problem in the unmanned aerial vehicle (UAV) system by fixed-time convergent reinforcement learning (RL). By virtue of the zero-sum game, the false data injection (FDI) attacker and secure controller are viewed as game players. T...

Full description

Saved in:
Bibliographic Details
Published in:IEEE/CAA journal of automatica sinica 2024-07, Vol.11 (7), p.1699-1701
Main Authors: Gong, Zhenyu, Yang, Feisheng
Format: Article
Language:English
Subjects:
Algorithms
Convergence
Tracking control
Unmanned aerial vehicles
Zero sum games
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Dear Editor, This letter is concerned with the secure tracking control problem in the unmanned aerial vehicle (UAV) system by fixed-time convergent reinforcement learning (RL). By virtue of the zero-sum game, the false data injection (FDI) attacker and secure controller are viewed as game players. Then, the attack-defense process is recast as a min-max problem. For solving the problem and acquiring the optimal secure control policy, a single-critic RL algorithm with fixed-time convergence is presented. Meanwhile, the associated convergence and stability proofs are given. A simulation is provided to show the effectiveness of the raised method.
ISSN:2329-9266
2329-9274
DOI:10.1109/JAS.2023.124149