Black-box adversarial attacks on deep reinforcement learning-based proportional–integral–derivative controllers for load frequency control
Regular Papers|Updated:2026-01-07
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Black-box adversarial attacks on deep reinforcement learning-based proportional–integral–derivative controllers for load frequency control
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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
“In the field of load frequency control, this study introduces its research progress. Expert developed the DRL-based adaptive controller system, which provides solutions to enhance the robustness of control systems under adversarial attacks.”
Frontiers of Information Technology & Electronic EngineeringVol. 26, Issue 11, Pages: 2128-2142(2025)
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
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:
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.
Black-box adversarial attacks on deep reinforcement learning-based proportional–integral–derivative controllers for load frequency controlEnhanced Publication