A Robust Learning Model for Deck Motion Prediction of Aircraft Carrier
The irregular deck motion of the aircraft carrier in six-degree freedom is generally caused by wind,waves,and currents,which affects the precision of aircraft landings.Aircraft carrier deck motion prediction and compensation are important functions of automatic landing systems as well as key technologies improving the safety and success rate of aircraft landing.In this paper,a robust learning model for deck motion prediction was presented,which constructs complex learning systems through the adaptive evolution of basic building blocks.The training of these building blocks employs a non-gradient pseudoinverse learning strategy,which improves training efficiency and simplifies the tuning of learning control hyperparameters.The architecture design of the building blocks adopts a data-driven approach,simplifying architectural hyperparameter tuning.A graph Laplace regularization term was employed in order to enhance the robustness of the model against noise and unexpected perturbations.Through simulation experiments conducted on a specific aircraft carrier cruising at a typical speed under moderate sea condi-tions,the effectiveness and robustness of the proposed method in predicting the pitch,roll,and heave of the deck are verified.
万方数据
维普
