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Robust optimal motion cueing algorithm based on the linear quadratic regulator method and a genetic algorithm

Asadi, Houshyar, Mohamed, Shady, Lim, Chee Peng and Nahavandi, Saeid 2016, Robust optimal motion cueing algorithm based on the linear quadratic regulator method and a genetic algorithm, IEEE transactions on systems, man, and cybernetics: systems, no. 99, pp. 1-1, doi: 10.1109/TSMC.2016.2523906.

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Title Robust optimal motion cueing algorithm based on the linear quadratic regulator method and a genetic algorithm
Author(s) Asadi, Houshyar
Mohamed, Shady
Lim, Chee Peng
Nahavandi, Saeid
Journal name IEEE transactions on systems, man, and cybernetics: systems
Issue number 99
Start page 1
End page 1
Total pages 17
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2016-02-26
ISSN 2168-2216
Summary The aim of this paper is to design and develop an optimal motion cueing algorithm (MCA) based on the genetic algorithm (GA) that can generate high-fidelity motions within the motion simulator's physical limitations. Both, angular velocity and linear acceleration are adopted as the inputs to the MCA for producing the higher order optimal washout filter. The linear quadratic regulator (LQR) method is used to constrain the human perception error between the real and simulated driving tasks. To develop the optimal MCA, the latest mathematical models of the vestibular system and simulator motion are taken into account. A reference frame with the center of rotation at the driver's head to eliminate false motion cues caused by rotation of the simulator to the translational motion of the driver's head as well as to reduce the workspace displacement is employed. To improve the developed LQR-based optimal MCA, a new strategy based on optimal control theory and the GA is devised. The objective is to reproduce a signal that can follow closely the reference signal and avoid false motion cues by adjusting the parameters from the obtained LQR-based optimal washout filter. This is achieved by taking a series of factors into account, which include the vestibular sensation error between the real and simulated cases, the main dynamic limitations, the human threshold limiter in tilt coordination, the cross correlation coefficient, and the human sensation error fluctuation. It is worth pointing out that other related investigations in the literature normally do not consider the effects of these factors. The proposed optimized MCA based on the GA is implemented using the MATLAB/Simulink software. The results show the effectiveness of the proposed GA-based method in enhancing human sensation, maximizing the reference shape tracking, and reducing the workspace usage.
Language eng
DOI 10.1109/TSMC.2016.2523906
Field of Research 091302 Automation and Control Engineering
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082901

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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