Identification of Motion Perception Thresholds in Active Control Tasks

Abstract

In the design of a high fidelity simulator environment, knowledge about motion perception thresholds is essential. Thresholds are generally measured in a passive experimental setup, in which subjects do not actively influence their motion profile. In this paper, a method for analytical identification of motion perception thresholds in active control tasks is proposed. The effect of vestibular motion on thresholds was analyzed and a comparison to conventional passive threshold measurements was made. The threshold identification method was based on a multi-channel pilot model extended with a nonlinear absolute threshold element. Maximum likelihood parameter estimation, combining a Genetic Algorithm and an unconstrained Gauss-Newton algorithm optimization, was applied. A theoretical study indicated that there is an upper limit to the vestibular motion amplitude to allow accurate threshold identification. Two experiments were performed in the SIMONA Research Simulator to test the application of the method. A passive experiment to measure the sensory pitch threshold and an active control task to identify the active pitch threshold. In the active experiment, the vestibular motion amplitude was varied and two types of control tasks were used. For the disturbance-rejection task, the pitch threshold was only identifiable for high motion amplitude levels. The target-tracking task allowed identification of the threshold for medium and high amplitude levels. Neither of the tasks allowed threshold identification with low levels of motion amplitude. The pitch thresholds obtained from the active and passive experiment were in the same order of magnitude

Tuning Models of Pilot Tracking Behavior for a Specific Simulator Motion Cueing Setting

“…5b still show a much smaller opposite trend with K S . As these trends are consistent with the results reported in a number of the individual studies that were included, 10,11,15,25 the following tuning rules are defined for ω c,d and ω c,t :…”

“…7,10,11,[24][25][26]28 This reduction in visual lead stems from the fact that the motion cues, which are, for instance, perceived with the vestibular system, provide information on the rates of the controlled element state directly, thereby removing the requirement to generate lead visually. 3,24 Fig.…”

“…In addition, compared to the case where no motion filter is present (H mf (jω) = 1), pilots may adapt their control dynamics in response to a motion filter with certain dynamics being introduced to (partially) compensate for the effect the motion filter dynamics have on the closed-loop system. The most elementary example that can be given is the case where motion cues are attenuated by a pure gain, H mf (jω) = K. As long as the gain does not cause the motion cues to become smaller than human motion perception thresholds, 10 pilots could simply respond to the lower magnitude motion information (ÿ s ) with a higher gain. If they succeed in increasing the gain of H pm (jω) with around 1/K, this means the governing open-loop and closed-loop dynamics remain approximately the same, as can be verified from Eqs.…”

“…For instance, a controlled element with double integrator dynamics as used in Ref. 10 requires markedly more pilot lead equalization (higher T L ) than typical aircraft pitch and roll dynamics as controlled in the experiment of Ref. 14.…”

“…4,8 This paper provides the first result of our effort in compiling data from a number of experiments from which measurements of multimodal pilot behavior under varying motion cueing conditions are available. Data have been collected from a number of investigations performed at Delft University of Technology 7,[9][10][11][12] and from a number of studies found in literature. 4,8,[13][14][15] This paper will provide a short overview of the scope and setup of all these different experiments.…”