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<p>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.</p>

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Black-box adversarial attacks on deep reinforcement learning-based proportional–integral–derivative controllers for load frequency control

  • 期刊论文

  • 发表日期：

    2025年11月1日

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  • Wei Wang,
  • Zhenyong Zhang,
  • Xin Wang,
  • Xuguo Jiao

摘要

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.