Autoscaling: Minimising Immersion Disruption in Motion Cueing Using Model Predictive Control

Abstract

Driving simulators aim to replicate real-world vehicle experiences by recreating accelerations acting on occupants using a combination of translational accelerations and tilt-coordination. Due to space constraints, translational accelerations alone are insufficient, and platform tilting generates additional gravitational forces to enhance realism. However, ensuring the tilt motion remains imperceptible is critical to maintaining immersion.
Model Predictive Control-based motion cueing algorithms demonstrate superior specific force tracking and platform workspace utilization. Despite these benefits, MPC algorithms can exhibit pre-positioning, a phenomenon where the platform tilts prematurely in anticipation of future motion, causing perceptible false cues that disrupt immersion. This phenomenon is particularly noticeable in tilt-coordination due to sustained specific forces.
This work proposes a solution to mitigate pre-positioning by introducing a dynamic scaling factor for tilt-coordination. By scaling down the reference signal for tilt coordination, it stays within the simulator’s tilt angle and tilt-rate capabilities, and platform tilt rates are kept below human perception thresholds. The scaling factor is derived from two key parameters: the maximum specific force generated by platform tilt and the tilt rate perception threshold. The reference for specific force is unscaled to optimally use the translational workspace.
This approach enhances driving simulator realism by minimizing the perceptibility of pre-positioning while optimizing specific force recreation. Subjective evaluations also indicate improved immersion, illustrating the effectiveness of the scenario-adaptive Autoscaling MCA.