Abstract
The Motion Cueing Algorithm (MCA) transforms longitudinal and rotational motions into simulator movement, aiming to regenerate high fidelity motion within the simulators physical limitations. Classical washout filters are widely used in commercial simulators because of their relative simplicity and reasonable performance. The main drawback of classical washout filters is the inappropriate empirical parameter tuning method that is based on trial-and-error, and is effected by programmers’ experience. This is the most important obstacle to exploiting the platform efficiently. Consequently, the conservative motion produces false cue motions. Lack of consideration for human perception error is another deficiency of classical washout filters and also there is difficulty in understanding the effect of classical washout filter parameters on generated motion cues. The aim of this study is to present an effortless optimization method for adjusting the classical MCA parameters, based on the Genetic Algorithm (GA) for a vehicle simulator in order to minimize human sensation error between the real and simulator driver while exploiting the platform within its physical limitations. The vestibular sensation error between the real and simulator driver as well as motion limitations have been taken into account during optimization. The proposed optimized MCA based on GA is implemented in MATLAB/Simulink. 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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Grant, P.R., Reid, L.D.: Motion washout filter tuning: rules and requirements. J. Aircr. 34, 145–151 (1997)
Hossny, M., Mohamed, S., Nahavandi, S.: Driver behaviour prediction for motion simulators using changepoint segmentation. Presented at the IEEE International Conference on Systems, Man, and Cybernetics, Hong Kong (2015)
Conrad, B., Schmidt, S.: A study of techniques for calculating motion drive signals for flight simulators. NASA CR-114345 (1971)
Conrad, B., Schmidt, S., Douvillier, J.: Washout circuit design for multi-degrees of freedom moving base simulators. In: Proceedings of the AiAA Visual and Motion Simulation Conference, Palo Alto, CA, 10 September 1973
Reid, L., Nahon, M.: Flight Simulation Motion-base Drive Algorithms: Part 1-Developing and Testing the Equations. Institute for Aerospace Studies, Toronto University, Toronto (1985)
Asadi, H., Mohammadi, A., Mohamed, S., Rahim Zadeh, D., Nahavandi, S.: Adaptive washout algorithm based fuzzy tuning for improving human perception. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds.) ICONIP 2014, Part III. LNCS, vol. 8836, pp. 483–492. Springer, Heidelberg (2014)
Song, J.-B., Jung, U.-J., Ko, H.-D.: Washout algorithm with fuzzy-based tuning for a motion simulator. KSME Int. J. 17, 221–229 (2003)
Parrish, R.V., Dieudonne, J.E., Martin Jr., D.J.: Coordinated adaptive washout for motion simulators. J. Aircr. 12, 44–50 (1975)
Sivan, R., Ish-Shalom, J., Huang, J.-K.: An optimal control approach to the design of moving flight simulators. IEEE Trans. Syst. Man Cybern. 12, 818–827 (1982)
Telban, R.J., Cardullo, F.M.: Motion cueing algorithm development: human-centered linear and nonlinear approaches. NASA TechReport CR-2005-213747 (2005)
Asadi, H., Mohamed, S., Nahavandi, S.: Incorporating human perception with the motion washout filter using fuzzy logic control. IEEE/ASME Trans. Mechatron. PP, 1–9 (2015)
Nehaoua, L., Amouri, A., Arioui, H.: Classic and adaptive washout comparison for a low cost driving simulator. In: Proceedings of the 2005 IEEE International Symposium on Intelligent Control, Mediterrean Conference on Control and Automation, pp. 586–591 (2005)
Nehaoua, L., Arioui, H., Mohellebi, H., Espie, S.: Restitution movement for a low cost driving simulator. In: American Control Conference 2006, p. 6 (2006)
Nehaoua, L., Mohellebi, H., Amouri, A., Arioui, H., Espié, S., Kheddar, A.: Design and control of a small-clearance driving simulator. IEEE Trans. Veh. Technol. 57, 736–746 (2008)
Asadi, H., Mohammadi, A., Mohamed, S., Nahavandi, S.: Adaptive translational cueing motion algorithm using fuzzy based tilt coordination. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds.) ICONIP 2014, Part III. LNCS, vol. 8836, pp. 474–482. Springer, Heidelberg (2014)
Asadi, H., Mohamed, S., Rahim Zadeh, D., Nahavandi, S.: Optimisation of nonlinear motion cueing algorithm based on genetic algorithm. Veh. Syst. Dyn. 1–20 (2015)
Nahon, M.A., Reid, L.D.: Simulator motion-drive algorithms-a designer’s perspective. J. Guidance Control Dyn. 13, 356–362 (1990)
Meiry, J., Young, L.: A revised dynamic otolith model (1968)
Zacharias, G.L.: Motion cue models for pilot-vehicle analysis. DTIC Document (1978)
Yang, X.-S.: Chapter 5 - genetic algorithms. In: Yang, X.-S. (ed.) Nature-Inspired Optimization Algorithms, pp. 77–87. Elsevier, Oxford (2014)
Kantardzic, M.: Data Mining: Concepts, Models, Methods, and Algorithms. Wiley, New York (2011)
Gosselin, L., Tye-Gingras, M., Mathieu-Potvin, F.: Review of utilization of genetic algorithms in heat transfer problems. Int. J. Heat Mass Transf. 52, 2169–2188 (2009)
Ferreira, A., da Silva, M.F., Cruz, C.D.: Estimating the effects of population size and type on the accuracy of genetic maps. Genet. Mol. Biol. 29, 187–192 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Asadi, H., Mohamed, S., Nelson, K., Nahavandi, S., Zadeh, D.R. (2015). Human Perception-Based Washout Filtering Using Genetic Algorithm. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9490. Springer, Cham. https://doi.org/10.1007/978-3-319-26535-3_46
Download citation
DOI: https://doi.org/10.1007/978-3-319-26535-3_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26534-6
Online ISBN: 978-3-319-26535-3
eBook Packages: Computer ScienceComputer Science (R0)