Analysis of pilot landing control in crosswind using neural networks

Human pilot control at visual approach has been analyzed using a neural network modeling technique. Neural network models simulate the relationship between control (e.g., elevator, throttle, etc.) and human input (visual information) and can be analyzed mathematically. In previous research, only longitudinal operation was analyzed, because the characteristic flare maneuver is said to be one of the most difficult maneuvers in normal operations. However, lateral control is also difficult especially under crosswind conditions. In a crosswind approach, crab control is applied first, and then winglow sideslip control is applied. The transition process of these two controls is called decrab, which is recognized as quite a difficult maneuver. Moreover, the longitudinal control also depends on the lateral control, and the influence of this coupling deserves some interest, too. In this paper we focus on the lateral control analysis. As in the previous research, neural network plot models are investigated with sensitivity analysis. Several simulator experiments were conducted with different pilots and under various wind conditions, and the analysis results clarify the differences in control strategies.