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NASA NTRS2021

Between-Simulator Comparison of Motion Filter Order and Break Frequency Interaction Effects on Pilot Control Behavior

This paper investigates the interaction effects of motion filter order and break frequency on pilots' manual control behavior and control performance using two simulators. Eighteen pilots performed the experiment in the Vertical Motion Simulator (VMS) at NASA Ames Research Center and twenty pilots in the SIMONA Research Simulator at Delft University of Technology. The experiment used a full-factorial design with three motion filter orders (first-, second-, and third-order) and two filter break frequencies (0.5 and 2.0 rad/s), in addition to reference no-motion and full-motion conditions. Key task variables, such as the quality of the motion and visual cues and the characteristics of the sidestick, were matched across both simulators. Overall, the expected effects of filter order and break frequency variations were found, with both increasing order and increasing break frequency causing pilots to use less motion feedback in their control strategy, resulting in lower tracking performance. Furthermore, across the wide range of filter orders tested in the experiment, the existing Sinacori-Schroeder motion fidelity criterion was found to be a good predictor of the interaction effects of both filter settings on pilot control behavior. For the same motion condition, there was a consistent offset in the results between simulators, due to the more high-gain control strategy adopted by a number of the VMS pilots. Still, the observed relative trends in pilot control behavior and performance between motion conditions were equivalent in both simulators and thus accurately replicated.

manual control↗

NASA NTRSDate not supplied

Linear Mixed-Effects Models for Human-in-the-Loop Tracking Experiment Data

Linear mixed-effects models provide several benefits over more traditional statistical inference tests that are particularly useful for most human-in-the-loop tracking experiment data. However, surprisingly, mixed models are virtually not used for the analysis of tracking experiment data. This paper uses of linear mixed-effects models to analyze combined tracking data from two previous human-in-the-loop roll tracking experiments that compared control behavior metrics collected in both a research aircraft and a motion-base simulator. In the experiments, pilots' behavior under 10 different motion configurations with varying motion filter gains and break frequencies was evaluated and compared to that in the real aircraft. The linear mixed-effects model analysis on the combined dataset confirmed the main statistical outcomes of the individual experiments. The main benefits of mixed models for this type of data were demonstrated by successfully combining data from two experiments that used different experimental conditions and of which one had an additional apparatus and the other a missing participant. Finally, the mixed-model analysis was able to explicitly test for scientifically relevant statistical differences in the dependent measures between the aircraft and simulator, as well as both experiments.

manual control↗