Abstract
In recent years, with the continuous upgrading of technology, flight accidents caused by aircraft failures have been greatly reduced. It has become the most important cause of major aviation accidents that pilots operate aircraft into complex state and fail to recover. It is very important to train pilots with UPRT, so it is urgent to improve the UPRT performance of existing flight simulators. This paper proposes an improved washout algorithm for UPRT scenario. The key point of the algorithm is to combine the advantages of the washout algorithm based on model predictive control and the classical washout algorithm. The washout algorithm based on model predictive control (MPC washout algorithm) can simulate large amplitude and low frequency motion better, and has greater space utilization, while the classical washout algorithm is efficient and simple, and has better simulation effect for small amplitude and high frequency motion. By setting the exponential weighted fusion algorithm to fuse the output of the two algorithms, it can have better dynamic simulation effect in large and small motion, and is more suitable for UPRT scenario. In addition, it also saves the time of designing and debugging algorithms or parameters separately for different missions.
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
Boeing, Statistical Summary of Commercial Jet Airplane Accidents Worldwide Operations, 1959–2017
Rosenkrans, W.: Brave new world: FAA simulator requirements enable acceptably realistic recoveries from full stalls and upsets in commercial jets. AeoSafety World, July-August 2016
Chang, Y.-H., Liao, C.-S., Chieng, W.-H.: Optimal motion cueing for 5-DOF motion simulations via a 3-DOF motion simulator. Control Eng. Pract. 17(1), 170–184 (2009)
Aminzadeh, M., Mahmoodi, A., Sabzehparvar, M.: Optimal motion cueing algorithm using motion system kinematics. Eur. J. Control 18(4), 363–375 (2012)
Lee, W.-S., Kim, J.-H., Cho, J.-H.: A driving simulator as a virtual reality tool. In: Proceedings of the 1998 IEEE International Conference on Robotics and Automation, vol. 1, pp. 71–76. IEEE (1998)
Telban, R.J., Cardullo, F.M.: Motion cueing algorithm development: human-centered linear and nonlinear approaches. Technical report, NASA/CR-2005–213747 (2005)
Asadi, H., Mohamed, S., Lim, C.P., Nahavandi, S., Nalivaiko, E.: Semicircular canal modeling in human perception. Rev. Neurosci. 28(5), 537–549 (2017)
Asadi, H., Mohamed, S., Lim, C.P., Nahavandi, S.: A review on otolith models in human perception. Behav. Brain Res. 309, 67–76 (2016)
Conrad, B., Schmidt, S.F.: 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, vol. 12 (1973)
Reid, L., Nahon, M.A.: Flight simulation motion-base drive algorithms: part 1. Developing and testing equations. University of Toronto, Technical report (1985)
Reid, L., Nahon, M.A.: Flight Flight simulation motion-base drive algorithms: part 2. Selecting the system parameters. University of Toronto, Technical report (1986)
Reid, L., Nahon, M.: Flight simulation motion-base drive algorithms. Part 3: Pilot evaluations (1986)
Parrish, R.V., Dieudonne, J.E., Martin Jr., D.J.: Coordinated adaptive washout for motion simulators. J. Aircr. 12(1), 44–50 (1975)
Sivan, R., Ish-Shalom, J., Huang, J.-K.: An optimal control approach to the design of moving flflight simulators. IEEE Trans. Syst. Man Cybern. 12(6), 818–827 (1982)
Song, J.-B., Jung, U.-J., Ko, H.-D.: Washout algorithm with fuzzy-based tuning for a motion simulator. KSME Int. J. 17(2), 221–229 (2003)
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. LNCS, vol. 8836, pp. 474–482. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12643-2_58
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. LNCS, vol. 8836, pp. 483–492. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12643-2_59
Dagdelen, M., Reymond, G., Kemeny, A., Bordier, M., Maizi, N.: Model-based predictive motion cueing strategy for vehicle driv-ing simulators. Control Eng. Pract. 17(19), 995–1003 (2009)
Garrett, N.J.I., Best, M.C.: Model predictive driving simulator motion cueing algorithm with actuator-based constraints. Veh Syst Dyn 51(8), 1151–1172 (2013). https://doi.org/10.1080/00423114.2013.783219
Mohammadi, A., Asadi, H., Mohamed, S., Nelson, K., Nahavandi, S.: MPC-based motion cueing algorithm with short prediction hori-zon using exponential weighting. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE (2016)
Salisbury, I.G., Limebeer, D.J.: Optimal motion cueing for race cars. IEEE Trans. Control Syst. Technol. 24(1), 200–215 (2016)
Mohammadi, A., Asadi, H., Mohamed, S., Nelson, K., Nahavandi, S.: Multiobjective and interactive genetic algorithms for weight tuning of a model predictive control-based motion cueing algorithm. IEEE Trans. Cybern. 49(9), 3471–3481 (2018)
Qazani, M.R.C., Asadi, H., Khoo, S., Nahavandi, S.: A linear time-varying model predictive control-based motion cueing algorithm for hexapod simulation-based motion platform. IEEE Trans. Syst. Man Cybern.: Syst. (2019)
Pool, D.M., Zaal, P.M.T., Damveld, H., van Paassen, M. M., Mulder, M.: Evaluating simulator motion fidelity using in-flight and simulator measurements of roll tracking behavior. In: AIAA Modeling and Simulation Technologies Conference, August 2012
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Tang, W., Wang, Z., Fu, S. (2021). An Improved Washout Algorithm for UPRT Scenario. In: Harris, D., Li, WC. (eds) Engineering Psychology and Cognitive Ergonomics. HCII 2021. Lecture Notes in Computer Science(), vol 12767. Springer, Cham. https://doi.org/10.1007/978-3-030-77932-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-77932-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77931-3
Online ISBN: 978-3-030-77932-0
eBook Packages: Computer ScienceComputer Science (R0)