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Black-box adversarial attack on deep reinforcement learning-based PID controller for load frequency control
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Wei WANG1, Zhenyong ZHANG1,2, Xin WANG2, Xuguo JIAO3,4. Black-box adversarial attack on deep reinforcement learning-based PID controller for load frequency control[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="Black-box adversarial attack on deep reinforcement learning-based PID controller for load frequency control",
author="Wei WANG1, Zhenyong ZHANG1,2, Xin WANG2, Xuguo JIAO3,4",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2401021"
}
%0 Journal Article
%T Black-box adversarial attack on deep reinforcement learning-based PID controller for load frequency control
%A Wei WANG1
%A Zhenyong ZHANG1
%A 2
%A Xin WANG2
%A Xuguo JIAO3
%A 4
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2401021
TY - JOUR
T1 - Black-box adversarial attack on deep reinforcement learning-based PID controller for load frequency control
A1 - Wei WANG1
A1 - Zhenyong ZHANG1
A1 - 2
A1 - Xin WANG2
A1 - Xuguo JIAO3
A1 - 4
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2401021
Abstract: load frequency control (LFC) is usually managed by traditional proportional integral derivative (PID)
controllers. Recently, deep reinforcement learning (DRL)-based adaptive controllers have been widely studied for
their superior performance. However, the DRL-based adaptive controller exhibits inherent vulnerability due to
adversarial attacks. To develop more robust control systems, this study conducts a deep analysis of DRL-based
adaptive controller vulnerability under adversarial attacks. First, an adaptive controller is developed based on the
DRL algorithm. Subsequently, considering the limited capability of attackers, the DRL-based LFC is evaluated
under adversarial attacks using the zeroth-order optimization (ZOO) method. Finally, we use adversarial training
to enhance the robustness of DRL-based adaptive controllers. Extensive simulations are conducted to evaluate the
performance of the DRL-based PID controller with and without adversarial attacks.
Open peer comments: Debate/Discuss/Question/Opinion
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