Have a personal or library account? Click to login
Methods to Determine Optimal Washout Position for Single and Multi-Occupant Motion Simulator Cover

Methods to Determine Optimal Washout Position for Single and Multi-Occupant Motion Simulator

Open Access
|Apr 2016

Abstract

The washout position has great effects on the perception fidelity and the movement of the motion simulator. In this paper, the main square error of sensation between simulator and vehicle is determined, and the fidelity of the occupant and the platform motion performance as a basis to build a model for a single occupant optimal washout position measurement. Then, considering the features of the multi-occupant motion simulator, a concept of the task’s master-crew is proposed, the multi-occupant optimal washout position measurement model is built. Then an anti-aircraft simulator for three occupants is taken as an example of a multi-occupant simulator and the optimal washout position measurement model is studied. Considering that the basic PSO algorithm has shortcomings such as easy aging and falling into local extreme value, a modified algorithm is designed called Adaptive Chaos PSO algorithm and applied to the resolution of the models. The results showed that the optimal position can make the simulator trainer have a more realistic sensation and maintain the safety of the platform, and that the Adaptive chaos PSO algorithm can effectively solve the shortcomings of PSO and is very useful in the resolution of the model.

DOI: https://doi.org/10.1515/cait-2016-0014 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
Language: English
Page range: 173 - 187
Published on: Apr 9, 2016
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2016 Xiang-Tong Kong, Yuan-Chang Zhu, Yan-Qiang Di, Hao-Hao Cui, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.