Kyle Nelson
Deakin University
16 Papers
84 Citations
Kyle Nelson is an academic researcher from Deakin University. The author has contributed to research in topics: Model predictive control & Motion simulator. The author has an hindex of 11, co-authored 16 publications.
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Papers
Multiobjective and Interactive Genetic Algorithms for Weight Tuning of a Model Predictive Control-Based Motion Cueing Algorithm
TL;DR: A clear method for obtaining the best MPC weighting has been proposed and the sensed motion error is minimized using the proposed method and with the same available workspace, a more realistic motion can be rendered to the driver.
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Optimizing Model Predictive Control horizons using Genetic Algorithm for Motion Cueing Algorithm
TL;DR: A novel method based on Genetic Algorithm is employed to achieve the best control and prediction horizons considering minimization of several terms such as sensation error, displacement and the computational burden, and the simulation results show the effectiveness of the proposed method.
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Vehicle motion simulators, a key step towards road vehicle dynamics improvement
TL;DR: In this article, the authors review existing road vehicle motion simulators and discuss each of the major subsystems related to the research and development of vehicle dynamics and explore the possibility of using motion simulator to conduct ride and handling test scenarios.
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Human Perception-Based Washout Filtering Using Genetic Algorithm
Houshyar Asadi,Shady Mohamed,Kyle Nelson,Saeid Nahavandi,Delpak Rahim Zadeh +4 more
- 09 Nov 2015
TL;DR: The results show the superiority of the proposed MCA as it improved the human sensation, maximized reference signal shape following and exploited the platform more efficiently within the motion constraints.
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MPC-based motion cueing algorithm with short prediction horizon using exponential weighting
Arash Mohammadi,Houshyar Asadi,Shady Mohamed,Kyle Nelson,Saeid Nahavandi +4 more
- 01 Jan 2016
TL;DR: Applying a nonuniform weighting method is proposed to stabilize the motion cueing algorithm using MPC with short prediction horizon and optimized weighting adjustment, and results show the effectiveness of the proposed method.
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