A Decoupled Linear Model Predictive Control-based Motion Cueing Algorithm for Simulation-based Motion Platform with Limitted Workspace

Abstract

The simulation-based motion platforms (SBMPs) are useful devices to reproduce the motion feeling for a driver/piolet. The SBMPs are restricted due to limitations of the platforms’ structures. These limitations lead discrepancy between visual and motion cues which can cause motion sickness. The motion cueing algorithm (MCA) is employed to reproduce motion cues of a real land and air vehicle in the SBMP within its physical limitations aiming to regenerate the realistic feeling for the SBMP’s driver. Recently, coupled linear model predictive control-based motion cueing algorithms (MPC-based MCA) have been proposed with consideration of the human perception model including semicircular canals and otolith organs. The decoupled linear model predictive control including semicircular canals and otolith organs have been designed in this study for the first time to use the limited linear workspace of the Hexapod-based motion simulation platform more effectively. The results of both the existing coupled and new decoupled MPC-based MCAs are compared. Finally, it is shown that the decoupled linear MPC-based MCA is able to reduce the motion sensation error between the real vehicle and SBMP drivers and also use the limited linear motion simulation platform workspace more efficiently compared to the existing coupled linear MPC-based MCA.