Future reference prediction in model predictive control based driving simulators
Mohammadi, Arash, Asadi, Houshyar, Mohamed, Shady, Nelson, Kyle and Nahavandi, Saeid 2016, Future reference prediction in model predictive control based driving simulators, in ACRA 2016 : Proceedings of the ARAA Australian Conference on Robotics and Automation, Australian Robotics and Automation Association, Brisbane, Qld..
Attached Files
Name
Description
MIMEType
Size
Downloads
Title
Future reference prediction in model predictive control based driving simulators
ACRA 2016 : Proceedings of the ARAA Australian Conference on Robotics and Automation
Publication date
2016
Total pages
8
Publisher
Australian Robotics and Automation Association
Place of publication
Brisbane, Qld.
Summary
The goal of a driving simulator is to produce an environment for a driver similar to the real driving scenario. Motion cueing algorithms are used to produce a realistic motion while respecting the workspace limitations and motion simulator boundaries. Model Predictive Control has become popular recently for motion cueing. However, in this control method, the optimization is based on a predefined constant future input trajectory while it is not a practical assumption. In this research, a method is proposed to predict the future reference based on the finite history of input. This method does not require the position trajectory to follow a specific road. Simulation results show the effectiveness of the proposed model predictive control method in terms of realistic motion sensation for a driver.
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.