New signal scaling strategies for return phase training in motion-base spaceflight simulator

Abstract

Signal scaling is an essential step in spaceflight simulation. Thus far, the third-order polynomial scaling method has been widely used for signal scaling; however, in this method, parameter tuning is complicated and may induce perceptible distortion during large-range monotonic signal scaling. In the simulation of spacecraft return, specifically, that of re-entry, acceleration and angular velocity signals may vary considerably over short time periods. Motion perception is important for training astronauts in this phase. In this study, two strategies are proposed to solve these problems using the 'scaling scope' parameter. The first strategy is based on the Hermite interpolation polynomial, and the other is based on thirdorder polynomial scaling. Two methods were developed which make use of the stable region of third-order polynomial scaling. The first method maximizes the stable region to prevent signal distortion, and the other restricts the scaling scope in the stable region. Based on the dynamic characteristics of spacecraft in the return phase, the signal scaling strategies proposed in this study are simulated for trainees' perception in a motion-base simulator. Simulations were implemented by utilizing the full curves of spacecraft return phase for the first time, and results show that these methods are more advantageous for parameter tuning and can eliminate signal distortion for all input signals. While these methods have a shortcoming in that the trigger velocity (onset cue) is slowed down, this shortcoming is eliminated by employing the moving cueing algorithm. Both the strategies proposed in this article show good performance and can be applied potentially to the motion simulation of the spacecraft return phase.

Evaluation of Vehicle Ride Height Adjustments Using a Driving Simulator

“…Nonlinear scaling of the vehicle input motions was used in this experiment based on the method in [27]. The scaling for the lateral motion of the vehicle is shown in Figure 3.…”

“…The output vector (tracking variables) consists of perceived acceleration, velocity and position represented by the rail ̂, , and hexapod ̂ℎ , ℎ , ℎ ; perceived acceleration represented through tilting ̂ and tilting degree ; total perceived acceleration ̂. Nonlinear scaling of the vehicle input motions was used in this experiment based on the method in [27]. The scaling for the lateral motion of the vehicle is shown in Figure 3.…”