Deakin University

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A decoupled linear model predictive control-based motion cueing algorithm for simulation-based motion platform with limitted workspace

conference contribution
posted on 2019-01-01, 00:00 authored by Mohammadreza Chalak Qazani, Houshyar AsadiHoushyar Asadi, Saeid Nahavandi
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.



IEEE Industrial Electronics Society. Conference (20th : 2019 : Melbourne, Vic.)


IEEE Industrial Electronics Society Conference


35 - 41


Institute of Electrical and Electronics Engineers


Melbourne, Vic.

Place of publication

Piscataway, N.J.

Start date


End date




Publication classification

E1 Full written paper - refereed

Copyright notice

2019, IEEE



Title of proceedings

ICIT 2019 : Proceedings of the 2019 20th IEEE International Conference on Industrial Technology