.png){kind=icon}
返回
Reinforcement learning-based secure synchronization for two-time-scale complex dynamical networks with malicious attacks
.png){kind=icon}
PRE
AI
DOI：10.1016/j.amc.2024.128840.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper studies the secure synchronization problem for two -time -scale complex dynamical networks with unknown dynamics information and malicious attacks. The challenge is that under complex dynamic networks with unknown dynamic information, all system matrices are unknown. To ameliorate this conundrum, we design a reinforcement learning algorithm, conjoined with a full -order processing of singularly perturbed parameters, with the express objective of securing the stability of the synchronization error system within a context devoid of adversarial intrusions. Second, in order to reduce the impact of malicious attacks, a switching function is developed based on feedback gain. Besides, it is proved that the proposed distributed controllers can still guarantee the convergence of the synchronization error system under malicious attacks. Finally, two numerical examples are given to illustrate the applicability and effectiveness of the proposed algorithm.
Keyword:
Two-time-scale systems
Complex dynamical networks
Reinforcement learning
Secure synchronization
期刊
IF:
3.4
论文数:
2.3W
被引数:
3.3W
机构
引用论文
Finite-time cluster synchronization for complex dynamical networks under FDI attack: A periodic control approach.png)
NEURAL NETWORKS
NEURAL NETWORKSIF6.3
PREAI
分享收藏
Adaptive optimal control for continuous-time linear systems based on policy iteration基于策略迭代的连续时间线性系统自适应最优控制AUTOMATICA
AUTOMATICAIF5.9
PREAI
分享收藏分享收藏分享收藏
查看更多内容