A Brief Overview of Optimal Robust Control Strategies for a Benchmark Power System with Different Cyberphysical Attacks

Security issue against different attacks is the core topic of cyberphysical systems (CPSs). In this paper, optimal control theory, reinforcement learning (RL), and neural networks (NNs) are integrated to provide a brief overview of optimal robust control strategies for a benchmark power system. First, the benchmark power system models with actuator and sensor attacks are considered. Second, we investigate the optimal control issue for the nominal system and review the state-of-the-art RL methods along with the NN implementation. Third, we propose several robust control strategies for different types of cyberphysical attacks via the optimal control design, and stability proofs are derived through Lyapunov theory. Furthermore, the stability analysis with the NN approximation error, which is rarely discussed in the previous works, is studied in this paper. Finally, two different simulation examples demonstrate the effectiveness of our proposed methods.