Eigenmode distortion is a novel quantitative methodology developed to objectively evaluate motion cueing fidelity in flight simulation. It relies on an explicit coupling of linearized vehicle and Motion Cueing Algorithm dynamics. Modal analysis subsequently performed on this coupled system reveals the degree of distortion imposed by the Motion Cueing Algorithm on to the dynamics of the simulated vehicle. Eigenmode distortion thereby provides unprecedented insight into the combined dynamics of the two systems along modal coordinates. Compared with existing methods for motion cueing fidelity assessment, the eigenmode distortion method enables a systematic analysis of the coupled vehicle and Motion Cueing Algorithm dynamics. This is mainly because it does not consider the Motion Cueing Algorithm in isolation and does not inherently rely on assumptions regarding the excitation of the simulated vehicle dynamics. This paper outlines the theoretical foundation of the eigenmode distortion method and includes a case study on helicopter longitudinal dynamics and a sensitivity analysis to demonstrate its utility. The results presented in this paper shown that the eigenmode distortion method can reveal interactions between the Motion Cueing Algorithm and the vehicle dynamics that are currently not captured by other established methods, such as the Sinacori–Schroeder criteria and the Objective Motion Cueing Test.@en

Numerous critical manual teleoperation tasks, such the control of the refueling boom during aerial refueling, require human controllers to accurately manipulate objects in the depth dimension, i.e., aligned with the viewing direction. To better understand the intricacies of depth control tasks and to be able to better support human controllers in such tasks, a cybernetic analysis of human control behavior in stereoscopic vision-enhanced depth control tasks would be a valuable extension of the current state-of-the-art in manual control research. This paper presents the initial findings of a human-in-the-loop experiment in which participants performed an abstract pursuit tracking task in which multisine target and disturbance forcing functions were used to facilitate cybernetic analysis of the measured control behavior. In terms of depth perception (i.e., perspective, viewing distance), the task was modeled after an aerial refueling scenario. Participants performed the pursuit tracking task for a reference "flat-plane" condition (task axis aligned with vertical screen axis) and depth tracking tasks either without stereoscopic cues, with natural stereoscopic vision, and with amplified hyperstereoscopic vision. Overall, the results of the experiment showed that participants achieved degraded task performance and less effective control dynamics in depth tracking tasks compared the the reference "flat-plane" condition. However, in line with earlier research on aerial refueling operator support systems, increased strength of the stereoscopic vision enhancements is found to enable much improved performance and increased human control gains. @en

Eigenmode distortion (EMD) is a novel methodology developed to study the degradation of perceived vehicle dynamics as a result of motion cueing algorithms (MCA’s) applied in rotorcraft 2ight simulators. This paper brie2y introduces EMD and subsequently describes its application in a pilot-in-the-loop experiment conducted on the SIMONA Research Simulator at Delft University of Technology. The experiment considers a precision hover task performed by two test pilots in three different motion cueing conditions. Each of the evaluated conditions is devised such to best reproduce one of the vehicle modes (pitch/heave subsidences and phugoid) simulated using an independently developed, three degree-of-freedom, longitudinal, nonlinear model of the AH-†„ Apache helicopter. The experiment yielded a number of interesting results. For example, the mode participation factors (MPFs) computed using recorded model states showed that the unstable phugoid mode dominates the overall dynamic response in all conditions evaluated. Also, based on the relative distribution of MPF’s across the three motion conditions, some indication of a change in pilot control behaviour as a result of motion cues (or lack thereof) was exposed. Finally, subjective pilot ratings suggest that the motion cueing condition optimized for the pitch subsidence mode is preferred, even though this is not the dominant mode in the vehicle’s response. The condition corresponding to the heave subsidence mode (i.e., only vertical motion cues) is appreciated least. @en

When coupled with additional degrees of freedom, centrifuge-based motion platforms can combine the agility of an hexapod-basedmotion platform with the ability of sustaining higher Glevels and an extended motion space. This combination of motion characteristics is required for realistic simulation of extreme flight scenarios. However, a false and often nauseating sensation of rotation, the so-called Coriolis effect, induced by the central yaw rotation, combined with the simultaneous rotation of the centrifuge cabin (passive Coriolis effect), or pilot’s head (active Coriolis effect), is the main disadvantage of any centrifuge-based motion platform. For this reason, the majority of human centrifuges are used solely as passive G-trainers in relatively short sessions. This paper discusses the development of a novel motion filter which aims to minimize the undesired Coriolis effects, by allowing for small mismatches in the alignment of pitch or roll coordination. Numerical studies showed that this Coherent Alignment Method (COHAM), is capable of reducing the angular accelerations, while constrained to operate within a region of coherent alignment, the Coherent Alignment Zone. In order obtain data to construct the CAZ region, i.e., establish body tilt thresholds in pitch and roll, an experiment was carried out in the Desdemona motion simulator. Results show higher thresholds in pitch and also higher ambiguity in pitch perception. A follow-up study is planned to further develop and experimentally validate our novel, predictive motion filter, based on the established CAZ region. @en