Data–Driven Model–Free Adaptive Dynamic Programming Resilient Control for Nonlinear Networked Control Systems Under DoS Attacks

Enhancing system security under denial-of-service (DoS) attacks requires robust compensation mechanisms. However, existing model-free adaptive control-based compensation solutions are limited to constant reference signals and neglect control optimization, causing insufficient tracking performance in dynamic attacks. This study develops a data-driven adaptive dynamic programming (ADP) resilient control scheme for networked control system under aperiodic DoS attacks. An ADP method with a modified performance index is proposed to derive a globally optimal controller, while a dynamic penalty factor is introduced to accelerate error convergence. Leveraging ADP technology and the latest available control increments, a compensation mechanism for time-varying reference signals is designed to reduce performance degradation. Finally, theoretical proofs ensure error convergence, and comparative simulations verify the strategy's superiority.