Frontiers of Information Technology & Electronic Engineering2025年26卷第11期 页码:2128-2142
Affiliations:
1.State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
2.Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
3.School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China
4.State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Author bio:
‡ Corresponding author
Funds:
National Natural Science Foundation of China(62303126;62362008;62203249);Major Scientific and Technological Special Project of Guizhou Province((2024)014);Open Project of the Key Laboratory of Computing Power Network and Information Security, Ministry of Education(2023ZD037);Taishan Scholars Program(tsqn202408239);Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China(ICT2025B39);Shandong Provincial Higher Education Youth Innovation Team Development Project(2022KJ290)
王威, 张镇勇, 王鑫, 等. 面向负载频率控制场景下深度强化学习比例—积分—微分控制器的黑盒对抗攻击[J]. Frontiers of Information Technology & Electronic Engineering, 2025,26(11):2128-2142.
Wei WANG, Zhenyong ZHANG, Xin WANG, et al. Black-box adversarial attacks on deep reinforcement learning-based proportional–integral–derivative controllers for load frequency control[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(11): 2128-2142.
王威, 张镇勇, 王鑫, 等. 面向负载频率控制场景下深度强化学习比例—积分—微分控制器的黑盒对抗攻击[J]. Frontiers of Information Technology & Electronic Engineering, 2025,26(11):2128-2142. DOI: 10.1631/FITEE.2401021.
Wei WANG, Zhenyong ZHANG, Xin WANG, et al. Black-box adversarial attacks on deep reinforcement learning-based proportional–integral–derivative controllers for load frequency control[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(11): 2128-2142. DOI: 10.1631/FITEE.2401021.