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MPC-based motion cueing algorithm with short prediction horizon using exponential weighting

Mohammadi, Arash, Asadi, Houshyar, Mohamed, Shady, Nelson, Kyle and Nahavandi, Saeid 2016, MPC-based motion cueing algorithm with short prediction horizon using exponential weighting, in SMC 2016 : Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, IEEE, Piscataway, N.J., pp. 1-6, doi: 10.1109/SMC.2016.7844292.

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Title MPC-based motion cueing algorithm with short prediction horizon using exponential weighting
Author(s) Mohammadi, Arash
Asadi, HoushyarORCID iD for Asadi, Houshyar orcid.org/0000-0002-3620-8693
Mohamed, ShadyORCID iD for Mohamed, Shady orcid.org/0000-0002-8851-1635
Nelson, KyleORCID iD for Nelson, Kyle orcid.org/0000-0003-1956-5493
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Conference name Systems, Man, and Cybernetics. IEEE International Conference (2016 : Budapest, Hungary)
Conference location Budpest, Hungary
Conference dates 9-12 Oct. 2016
Title of proceedings SMC 2016 : Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics
Publication date 2016
Conference series Systems, Man, and Cybernetics IEEE International Conference
Start page 1
End page 6
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) motion cueing algorithm
model predictive control
optimization
tuning
Summary A motion simulator is an effective tool for traininga driver in a safe environment by mimicking motion similarto the real world. To give a realistic feeling of driving andavoid motion sickness, an accurate motion cueing algorithm isrequired to restrict the platform within the allowed workspacerange while regenerating an appropriate motion feeling for thesimulator driver. Recently, employing Model Predictive Control(MPC) in the motion cueing algorithm has become popular. Inthis control method, by predicting future dynamics, an input isoptimized to minimize a cost function over a prediction horizonwhile respecting the constraints. Reducing the prediction horizonis desirable to minimize the computational burden; however itdraws the system toward instability. In this research, applying anonuniform weighting method is proposed to stabilize the motioncueing algorithm using MPC with short prediction horizon andoptimized weighting adjustment. Simulation results show theeffectiveness of the proposed method.
ISBN 9781509018970
Language eng
DOI 10.1109/SMC.2016.7844292
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 970110 Expanding Knowledge in Technology
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30090975

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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