Optimisation of nonlinear motion cueing algorithm based on genetic algorithm

Asadi, Houshyar, Mohamed, Shady, Zadeh, Delpak Rahim and Nahavandi, Saeid 2015, Optimisation of nonlinear motion cueing algorithm based on genetic algorithm, Vehicle system dynamics, vol. 53, no. 4, pp. 526-545, doi: 10.1080/00423114.2014.1003948.

Attached Files
Name Description MIMEType Size Downloads

Title Optimisation of nonlinear motion cueing algorithm based on genetic algorithm
Author(s) Asadi, HoushyarORCID iD for Asadi, Houshyar orcid.org/0000-0002-3620-8693
Mohamed, ShadyORCID iD for Mohamed, Shady orcid.org/0000-0002-8851-1635
Zadeh, Delpak Rahim
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Journal name Vehicle system dynamics
Volume number 53
Issue number 4
Start page 526
End page 545
Total pages 20
Publisher Taylor & Francis
Place of publication Oxford, Eng.
Publication date 2015
ISSN 0042-3114
Keyword(s) genetic algorithm
human sensation
motion cueing algorithm
nonlinear high-pass filter
washout filter
Summary Motion cueing algorithms (MCAs) are playing a significant role in driving simulators, aiming to deliver the most accurate human sensation to the simulator drivers compared with a real vehicle driver, without exceeding the physical limitations of the simulator. This paper provides the optimisation design of an MCA for a vehicle simulator, in order to find the most suitable washout algorithm parameters, while respecting all motion platform physical limitations, and minimising human perception error between real and simulator driver. One of the main limitations of the classical washout filters is that it is attuned by the worst-case scenario tuning method. This is based on trial and error, and is effected by driving and programmers experience, making this the most significant obstacle to full motion platform utilisation. This leads to inflexibility of the structure, production of false cues and makes the resulting simulator fail to suit all circumstances. In addition, the classical method does not take minimisation of human perception error and physical constraints into account. Production of motion cues and the impact of different parameters of classical washout filters on motion cues remain inaccessible for designers for this reason. The aim of this paper is to provide an optimisation method for tuning the MCA parameters, based on nonlinear filtering and genetic algorithms. This is done by taking vestibular sensation error into account between real and simulated cases, as well as main dynamic limitations, tilt coordination and correlation coefficient. Three additional compensatory linear blocks are integrated into the MCA, to be tuned in order to modify the performance of the filters successfully. The proposed optimised MCA is implemented in MATLAB/Simulink software packages. The results generated using the proposed method show increased performance in terms of human sensation, reference shape tracking and exploiting the platform more efficiently without reaching the motion limitations.
Language eng
DOI 10.1080/00423114.2014.1003948
Field of Research 090602 Control Systems, Robotics and Automation
Socio Economic Objective 970101 Expanding Knowledge in the Mathematical Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2015, Taylor & Francis
Persistent URL http://hdl.handle.net/10536/DRO/DU:30076652

Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 25 times in TR Web of Science
Scopus Citation Count Cited 36 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 613 Abstract Views, 4 File Downloads  -  Detailed Statistics
Created: Wed, 09 Sep 2015, 12:41:38 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.