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

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.

Keywords

Adaptive controller / Deep reinforcement learning / Load frequency control / Adversarial attacks

RIGHTS & PERMISSIONS

Zhejiang University Press