Analysis of Human Pilot Control Inputs Using Neural Network

Abstract

A neural-network-modeling approach is applied to analyze human-pilot control inputs during the landing phase in the visual approach. Flight data that contain the aircraft state variables and pilot control inputs are recorded using a flight simulator. The time history of visual cues and control inputs is utilized as teaching data for neural networks that can emulate the movements of a human pilot. A genetic algorithms approach is proposed to improve the generalization ability of the network by determining the network structure and initial values of its parameters. Generalization capabilities are evaluated by analyzing the flight data of a personal-computer-based simulator. The contribution ratios of each visual cue and their sensitivities to the control inputs of a pilot are estimated by analyzing the obtained neural-network models using a training simulator. The obtained results reveal that the proposed method can be used for analyzing the skill of a pilot.