Data-Driven Decentralized Resilient Control for Large-Scale Systems Under DoS Attacks

Abstract

This paper investigates the data-driven decentralized resilient control problem for large-scale systems (LSS) under randomly occurring Denial-of-Service (DoS) attacks. A min-max optimization criterion is established based on zero-sum differential game theory, and the corresponding optimal control strategy is derived. Global asymptotic stability of the closed-loop LSS is theoretically guaranteed under the proposed control scheme. A two-stage adaptive dynamic programming (ADP) algorithm, integrating reinforcement learning techniques with local state feedback, is proposed to derive the optimal control policy without requiring prior knowledge of the system model. Simulations are conducted in MATLAB on a multimachine power system benchmark. In particular, the two-stage ADP controller shortens the settling time by up to 7.7% and reduces overshooting by over 14.5% compared to the existing methods, thereby validating its robustness and superior performance in dynamic and adversarial environments.