Self‐Learning Robust Fault‐Tolerant Control for Aircraft Carrier Landing Using ADHDP

The direct lift control (DLC) strategy, while effective for decoupling trajectory and attitude control in an automatic carrier landing system (ACLS), is challenged by external disturbances, model uncertainties, and actuator faults. To address these limitations, this paper proposes a robust and self‐learning fault‐tolerant control (FTC) method for ACLS. First, an adaptive nonsingular fast terminal sliding mode control (ANFTSMC) method is employed as the baseline controller to ensure stable tracking in the presence of model uncertainties, external disturbances, and actuator faults. Then, an action‐dependent heuristic dynamic programming (ADHDP)‐based auxiliary controller is introduced, which leverages self‐learning capabilities to compensate for residual errors and enhance control performance without requiring an accurate system model. By synthesizing the baseline and auxiliary controllers, a robust self‐learning FTC is established, improving the adaptability and fault tolerance of the ACLS under the DLC strategy. The effectiveness of the proposed method is validated through Lyapunov stability analysis and simulation results, demonstrating its potential for aircraft carrier landings under complex conditions.