Eigenmode Distortion Analysis for Motion Cueing Evaluation in Fixed-Wing Aircraft Simulators

Master Thesis (2019)
Author(s)

S.L. Stoev (TU Delft - Aerospace Engineering)

Contributor(s)

MM van Paassen – Mentor (TU Delft - Control & Simulation)

Olaf Stroosma – Graduation committee member (TU Delft - Control & Simulation)

M Mulder – Graduation committee member (TU Delft - Control & Operations)

Faculty
Aerospace Engineering
Copyright
© 2019 Stanimir Stoev
More Info
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Publication Year
2019
Language
English
Copyright
© 2019 Stanimir Stoev
Graduation Date
14-01-2019
Awarding Institution
Delft University of Technology
Programme
Aerospace Engineering | Control & Simulation
Faculty
Aerospace Engineering
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Abstract

The Eigenmode Distortion (EMD) analysis is a novel method for objective evaluation of simulator motion cueing fidelity, developed at Delft University of Technology. It expresses the distortions of the perceived motion cues in terms of the dynamic modes of a linear model of the vehicle and has been applied to assess rotorcraft simulations. This thesis presents the adaptation of EMD for fixed wing aircraft, including performing the analysis at the pilot station instead of the centre of gravity. The method is applied to a combined linear model of a Cessna Citation 500 aircraft and the Classical Washout Algorithm (CWA). EMD is compared to the current state-of-the-art objective method, the Objective Motion Cueing Test (OMCT), which does not consider the dynamics of the simulated vehicle in its analysis. The two methods show different results in their cueing fidelity assessment of four CWA configurations. An experiment with six pilots is performed in the SIMONA Research Simulator to test the capability of EMD and OMCT to predict the cueing fidelity as perceived by pilots. The subjects perform pairwise comparisons between the four CWA configurations by exciting the short period dynamics of the aircraft. Results indicate that preferences vary considerably between pilots, causing both EMD and OMCT to show poor, but similar, predictive capabilities.

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