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. Attached Files Name Description MIMEType Size Downloads Title MPC-based motion cueing algorithm with short prediction horizon using exponential weighting Author(s) Mohammadi, Arash orcid.org/0000-0001-7634-8212 Asadi, Houshyar orcid.org/0000-0002-3620-8693 Mohamed, Shady orcid.org/0000-0002-8851-1635 Nelson, Kyle orcid.org/0000-0003-1956-5493 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 Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 01 Feb 2017, 10:41:44 EST Wed, 01 Feb 2017, 10:42:15 EST Thu, 02 Feb 2017, 04:01:00 EST Tue, 21 Mar 2017, 12:45:10 EST Wed, 22 Mar 2017, 05:06:05 EST Sat, 08 Apr 2017, 02:30:15 EST Fri, 19 May 2017, 08:12:22 EST Fri, 19 May 2017, 08:17:18 EST Fri, 19 May 2017, 08:19:09 EST Sat, 08 Jul 2017, 02:30:45 EST Fri, 04 Aug 2017, 10:05:12 EST Filtered Full Citation counts:   Cited 12 times in TR Web of Science Cited 17 times in Scopus Search Google Scholar Access Statistics: 420 Abstract Views, 7 File Downloads  -  Detailed Statistics Created: Tue, 21 Mar 2017, 12:45:10 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.