Designing an evolutionary optimal washout filter based on genetic algorithm

    Alireza Gharib Affiliation
    ; Masoud Goharimanesh   Affiliation
    ; Ali Koochi   Affiliation
    ; Mohammad Reza Gharib   Affiliation


This paper aims to design a reliable filter that can transform the actual motion of a flight simulator maneuver into a logical and understandable movement for its workspace. Motion cueing algorithms are used in scaling maneuvers to improve the user’s perception of real-world motion. As a unique algorithm, the washout-filter algorithm reduces the real motions where the user cannot understand the difference between the actual and simulated maneuvers. To design a proper washout filter, first, apply the inner ear model where humans can feel the motion to design a proper filter. The Otolith and semicircular systems were represented by two parts in this model. Second, an evolutionary theory based on a genetic algorithm is used to design a structure that minimizes human perception error and workspace boundaries. The issue is determining the coefficients in the model in order to create a high-performance flight simulator. The filtering algorithm, based upon the human vestibular model, compares human perception with flight simulator motion knowledge. The findings demonstrate an objective function that minimizes user perception error, and the flight simulator motion range can prepare a reliable washout filter for motion cueing.

Keyword : washout filter, optimal theory, genetic algorithm, modeling, control, flight simulator

How to Cite
Gharib, A., Goharimanesh, M., Koochi, A., & Gharib, M. R. (2022). Designing an evolutionary optimal washout filter based on genetic algorithm. Aviation, 26(1), 54–63.
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Mar 31, 2022
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Affan, M., Ahmed, S. U., Manek, A. I. y., & Uddin, R. (2019, 11–12 December). Design and implementation of the washout filter for the Stewart-Gough Motion Platform. In 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) (pp. 415–419). Dubai, UAE.

Arioui, H., Nehaoua, L., & Amouri, A. (2005). Classic and adaptive washout comparison for a low cost driving simulator. In Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation (pp. 586–591). IEEE.

Asadi, H., Lim, C. P., Mohamed, S., Nahavandi, D., & Nahavandi, S. (2019). Increasing motion fidelity in driving simulators using a fuzzy-based washout filter. IEEE Transactions on Intelligent Vehicles, 4(2), 298–308.

Asadi, H., Mohamed, S., Lim, C. P., & Nahavandi, S. (2016). Robust optimal motion cueing algorithm based on the linear quadratic regulator method and a genetic algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems, PP(99), 1–17.

Chen, S. H., & Fu, L.-C. (2011, August). An optimal washout filter design with fuzzy compensation for a motion platform. In 18th IFAC World Congress, IFAC Proceedings Volumes, 44(1), 8433–8438.

Chen, S. H., & Fu, L. C. (2010). An optimal washout filter design for a motion platform with senseless and angular scaling maneuvers. IFAC Proceedings Volumes, 44(1), 8433–8438.

Gharib, M. R., Kamelian, S., Seyyed Mousavi, S. A., & Dabzadeh, I. (2011). Modelling and multivariable robust controller for a power plant. International Journal of Advanced Mechatronic Systems, 3(2), 119–128.

Gharib, M. R. (2020). Comparison of robust optimal QFT controller with TFC and MFC controller in a multi-input multi-output system. Reports in Mechanical Engineering, 1(1), 151–161.

Gharib, M. R., Koochi, A., & Ghorbani, M. (2021). Path tracking control of electromechanical micro-positioner by considering control effort of the system. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 235(6), 984–991.

Goharimanesh, M., & Akbari, A. (2015). Optimum parameters of nonlinear integrator using design of experiments based on Taguchi method. Journal of Computational Applied Mechanics, 46(2), 233–241.

Goharimanesh, M., Akbari, A., & Akbarzadeh Tootoonchi, A. (2014). More efficiency in fuel consumption using gearbox optimization based on Taguchi method. Journal of Industrial Engineering International, 10(2), 61.

Grant, P. R., & Reid, L. D. (1997). Motion washout filter tuning: Rules and requirements. Journal of Aircraft, 34(2), 145–151.

Huang, C., & Fu, L. (2006, 8–11 October 2006). Human Vestibular Based (HVB) Senseless maneuver optimal washout filter design for VR-based motion simulator. In 2006 IEEE International Conference on Systems, Man and Cybernetics (pp. 4451–4458). Taipei, Taiwan.

Javadpour, S. M., Abbasi Jannat Abadi, E., Akbari, O. A., & Goharimanesh, M. (2020). Optimization of geometry and nano-fluid properties on microchannel performance using Taguchi method and genetic algorithm. International Communications in Heat and Mass Transfer, 119, 104952.

Kim, K. D., Kim, M. S., Moon, Y. G., & Lee, M. C. (2006, October 18, 2006–October 21). Application of vehicle driving simulator using new washout algorithm and robust control. In 2006 SICE-ICASE International Joint Conference (pp. 2121–2126). Busan, Korea, Republic of.

Kong, X.-T., Zhu, Y.-C., Di, Y.-Q., & Cui, H.-H. (2016). Methods to determine optimal washout position for single and multi-occupant motion simulator. Cybernetics and Information Technologies, 16(1), 173–187.

Liao, C.-S., Huang, C.-F., & Chieng, W.-H. (2004). A novel washout filter design for a six degree-of-freedom motion simulator. JSME International Journal Series C Mechanical Systems, Machine Elements and Manufacturing, 47(2), 626–636.

Liu, Z., Guo, Q., Jin, Z., & Yu, G. (2020). Research on a washout algorithm for 2-DOF motion platforms. In J. Y. C. Chen & G. Fragomeni (Eds). Virtual, augmented and mixed reality. Design and Interaction. HCII 2020. Lecture Notes in Computer Science, Vol. 12190. Springer.

Moavenian, M., Gharib, M. R., Daneshvar, A., & Alimardani, S. (2011, August). Control of human hand considering uncertainties. In The 2011 International Conference on Advanced Mechatronic Systems (pp. 17–22). IEEE.

Mohammadi, A., Asadi, H., Mohamed, S., Nelson, K., & Nahavandi, S. (2018a). Multiobjective and interactive genetic algorithms for weight tuning of a model predictive control-based motion cueing algorithm. IEEE Transactions on Cybernetics, 49(9), 3471–3481.

Mohammadi, A., Asadi, H., Mohamed, S., Nelson, K., & Nahavandi, S. (2018b). Optimizing model predictive control horizons using genetic algorithm for motion cueing algorithm. Expert Systems with Applications, 92, 73–81.

Nahon, M. A., Reid, L. D., & Kirdeikis, J. (1992). Adaptive simulator motion software with supervisory control. Journal of Guidance, Control, and Dynamics, 15(2), 376–383.

Nehaoua, L., Mohellebi, H., Amouri, A., Arioui, H., Espie, S., & Kheddar, A. (2008). Design and control of a small-clearance driving simulator. IEEE Transactions on Vehicular Technology, 57(2), 736–746.

Parrish, R. V., Dieudonne, J. E., Bowles, R. L., & Martin Jr, D. J. (1975). Coordinated adaptive washout for motion simulators. Journal of Aircraft, 12(1), 44–50.

Qazani, M. R. C., Asadi, H., Bellmann, T., Mohamed, S., Lim, C. P., & Nahavandi, S. (2020). Adaptive washout filter based on fuzzy logic for a motion simulation platform with consideration of joints limitations. In IEEE Transactions on Vehicular Technology, 69(11), 12547–12558.

Salehi Kolahi, M. R., Gharib, M. R., & Heydari, A. (2021). Design of a non-singular fast terminal sliding mode control for second-order nonlinear systems with compound disturbance. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 235(24), 7343–7352.

Salehi Kolahi, M. R., Gharib, M. R., & Koochi, A. (2021). Design of a robust control scheme for path tracking and beyond pull-in stabilization of micro/nano-positioners in the presence of Casimir force and external disturbances. Archive of Applied Mechanics, 91(10), 4191–4204.

Samavi, J., Goharimanesh, M., Akbari, A., & Dezyani, E. (2018). Optimisation of drilling parameters on St37 based on Taguchi method. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40(8), 370.

Sivan, R., Ish-Shalom, J., & Huang, J. (1982). An optimal control approach to the design of moving flight simulators. IEEE Transactions on Systems, Man, and Cybernetics, 12(6), 818–827.

Song, C.-C., Liaw, D.-C., & Chung, W.-C. (2002, October 28–October 31). Washout-filter based bifurcation control of longitudinal flight dynamics. In 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power (pp. 1646–1649). Beijing, China.

Telban, R., Cardullo, F., & Houck, J. A. (2002, August 5). Nonlinear, human-centered approach to motion cueing with a neurocomputing solver. In AIAA Modeling and Simulation Technologies Conference and Exhibit (pp. 1–10). Monterey, CA, USA.

Wang, S. C., & Fu, L. C. (2004, October 10, 2004–October 13). Predictive washout filter design for VR-based motion simulator. In IEEE International Conference on Systems, Man and Cybernetics, SMC 2004, 6291–6295. The Hague, Netherlands.

Wang, X. L., Li, L., & Zhang, W. (2008). Parameters optimization of the classical washout algorithm in locomotive driving simulator. Zhongguo Tiedao Kexue/China Railway Science, 29(5), 102–107.

Wang, X. L., Li, L., & Zhang, W. H. (2010). Research on fuzzy adaptive washout algorithm of train driving simulator. Tiedao Xuebao/Journal of the China Railway Society, 32(2), 31–36.

Yang, Y., Huang, Q.-T., & Han, J.-W. (2010). Adaptive washout algorithm based on the parallel mechanism motion range. Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 32(12), 2716–2720.