Data-driven Pilot Behavior Modeling Applied to a VMCG Determination Flight Test Task

Human models have been studied and used in engineering analysis for over 70 years to allow predictions of the pilot-vehicle system behavior. The difficulties in pilot modeling are evident due to the complexity of the brain, lack of repeatability in behavior and the great number of variables that can affect the human performance. This complexity, associated with the fact that there are no explicit laws to allow modeling based in first principles, could indicate that data-driven modeling techniques would be the most efficient way to obtain pilot models, such as black-box system identification methods that construct dynamic models according to measured input and output data, and where the parameters have no physical meaning. With this approach, it is advantageous to seek knowledge from other fields to allow a better understanding of the pilot behavior, select adequate input/output variables and define the experimental conditions and data. Criteria for evaluating the modeling approaches include adaptability as well as feasibility. Adaptability concerns coping with dynamic and uncertain conditions and feasibility refers to the models contribution to an applied context. This paper presents the results of the application of data-driven theoretical linear dynamic models in the task of representing the behavior of the pilot trying to keep the centerline of the runway after an engine failure. Real data is used, where PID with anti-windup and Hammerstein-Wiener model structures are compared. Results show that the Hammerstein-Wiener structure seems more appropriate to represent this specific behavior.Design and Comissioning of a Human Factors Laboratory for Aeronautic