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 NCS 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.