A genetic algorithm–based nonlinear scaling method for optimal motion cueing algorithm in driving simulator

Abstract

A motion cueing algorithm plays an important role in generating motion cues in driving simulators. The motion cueing algorithm is used to transform the linear acceleration and angular velocity of a vehicle into the translational and rotational motions of a simulator within its physical limitation through washout filters. Indeed, scaling and limiting should be used along within the washout filter to decrease the amplitude of the translational and rotational motion signals uniformly across all frequencies through the motion cueing algorithm. This is to decrease the effects of the workspace limitations in the simulator motion reproduction and improve the realism of movement sensation. A nonlinear scaling method based on the genetic algorithm for the motion cueing algorithm is developed in this study. The aim is to accurately produce motions with a high degree of fidelity and use the platform more efficiently without violating its physical limitations. To successfully achieve this aim, a third-order polynomial scaling method based on the genetic algorithm is formulated, tuned, and implemented for the linear quadratic regulator–based optimal motion cueing algorithm. A number of factors, which include the sensation error between the real and simulator drivers, the simulator’s physical limitations, and the sensation signal shape-following criteria, are considered in optimizing the proposed nonlinear scaling method. The results show that the proposed method not only is able to overcome problems pertaining to selecting nonlinear scaling parameters based on trial-and-error and inefficient usage of the platform workspace, but also to reduce the sensation error between the simulator and real drivers, while satisfying the constraints imposed by the platform boundaries.

Keywords

Motion cueing algorithm nonlinear scaling human sensation washout filter genetic algorithm

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References

Arioui

Nehaoua

Hima

et al . Mechatronics, design, and modeling of a motorcycle riding simulator. IEEE: ASME T Mech 2010; 15(5): 805–818.

Asadi

Mohamed

Nahavandi

. Incorporating human perception with the motion washout filter using fuzzy logic control. IEEE: ASME T Mech 2015; 20(6): 3276–3284.

Asadi

Mohammadi

Mohamed

et al . Adaptive translational cueing motion algorithm using fuzzy based tilt coordination. In: Loo

Yap

Wong

et al . (eds) Neural information processing. Berlin: Springer, 2014, pp.474–482.

Asadi

Mohammadi

Mohamed

et al . In: Loo

Yap

Wong

et al . (eds) Neural information processing. Berlin: Springer, 2014, pp.483–492.

Asadi

Mohamed

Rahim Zadeh

et al . Optimisation of nonlinear motion cueing algorithm based on genetic algorithm. Veh Syst Dynam 2015; 53; 526–545.

Nehaoua

Mohellebi

Amouri

et al . Design and control of a small-clearance driving simulator. IEEE T Veh Technol 2008; 57(2): 736–746.

Nehaoua

Amouri

Arioui

. Classic and adaptive washout comparison for a low cost driving simulator. In: Proceedings of the 13th IEEE international symposium on Mediterranean conference on control and automation, Limassol, Cyprus, 27–29 June 2005, pp.586–591. New York: IEEE.

Reid

Nahon

. Flight simulation motion-base drive algorithms: part 1—developing and testing the equations. Toronto, ON, Canada: Institute for Aerospace Studies, Toronto University, 1985.

Asadi

Mohamed

Nelson

et al . Human perception-based washout filtering using genetic algorithm. In: Proceedings of the international conference on neural information processing, Istanbul, Turkey, 9–12 November 2015, pp.401–411. Berlin: Springer.

10.

Asadi

Mohammadi

Mohamed

et al . A particle swarm optimization-based washout filter for improving simulator motion fidelity. In: Proceedings of the 2016 IEEE international conference on systems, man, and cybernetics (SMC), Budapest, 9–12 October 2016, pp.1963–1968. New York: IEEE.

11.

Asadi

. Human perception-based washout filtering. Waurn Ponds VIC, Australia: Deakin University, 2015.

12.

Nahon

Reid

. Simulator motion-drive algorithms—a designer’s perspective. J Guid Contr Dynam 1990; 13(2): 356–362.

13.

Sivan

Ish-Shalom

Huang

J-K

. An optimal control approach to the design of moving flight simulators. IEEE T Syst Man Cy 1982; 12(6): 818–827.

14.

Zacharias

. Motion cue models for pilot-vehicle analysis. Fort Belvoir, VA: DTIC, 1978.

15.

Hosman

Van der Vaart

. Vestibular models and thresholds of motion perception: results of tests in a flight simulator. Delft: Delft University of Technology, 1978.

16.

Asadi

Mohamed

Nelson

et al . A linear quadratic optimal motion cueing algorithm based on human perception. In: Proceedings of Australasian conference on robotics and automation ACRA 2014, Melbourne, VIC, Australia, 2–4 December 2014, pp.1–9. Melbourne, VIC, Australia: Australian Robotics and Automation Association.

17.

Telban

Cardullo

. Motion cueing algorithm development: human-centered linear and nonlinear approaches. NASA technical report no. CR-2005-213747, May2005, https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20050180246.pdf

18.

Asadi

Mohamed

Lim

et al . Robust optimal motion cueing algorithm based on the linear quadratic regulator method and a genetic algorithm. IEEE T Syst Man Cy: Syst 2016; 47(2): 238–254.

19.

Asadi

Mohamed

Nelson

et al . An optimal washout filter based on genetic algorithm compensators for improving simulator driver perception. In: Proceedings of the driving simulation conference & exhibition DSC 2015, Tübingen, 16–18 September 2015, pp.173–182. Munich: Max Planck Institute for the Advancement of Science.

20.

Dagdelen

Reymond

Kemeny

et al . Model-based predictive motion cueing strategy for vehicle driving simulators. Contr Eng Pract 2009; 17(9): 995–1003.

21.

Dagdelen

Reymond

Kemeny

et al . MPC based motion cueing algorithm: Development and application to the ULTIMATE driving simulator. In: Proceedings of the DSC Europe, Paris, France, September 2004, pp.221–233.

22.

Fang

Kemeny

. Explicit MPC motion cueing algorithm for real-time driving simulator. In: Proceedings of the 2012 7th international power electronics and motion control conference (IPEMC), Harbin, China, 2–5 June 2012, vol. 2, pp.874–878. New York: IEEE.

23.

Garrett

Best

. Driving simulator motion cueing algorithms—a survey of the state of the art, 2010, https://pdfs.semanticscholar.org/c52a/1880c1e53cd9e9e529db7f160bfd2087fd22.pdf

24.

Mohammadi

Asadi

Mohamed

et al . MPC-based motion cueing algorithm with short prediction horizon using exponential weighting. In: Proceedings of the 2016 IEEE international conference on systems, man, and cybernetics (SMC), Budapest, 9–12 October 2016, pp.521–526. New York: IEEE.

25.

Mohammadi

Asadi

Nelson

et al . Future reference prediction in model predictive control based driving simulators. In: Proceedings of the Australasian conference on robotics and automation (ACRA2016), Brisbane, QLD, Australia, 5–6 December 2016. Brisbane, QLD, Australia: ACRA.

26.

Mohammadi

Asadi

Mohamed

et al . Optimizing model predictive control horizons using genetic algorithm for motion cueing algorithm. Expert Syst Appl 2018; 92: 73–81.

27.

Asadi

Mohamed

Lim

et al . A review on otolith models in human perception. Behav Brain Res 2016; 309: 67–76.

28.

Asadi

Mohamed

Chee

et al . Semicircular canal modeling in human perception. Rev Neurosci 2017; 28: 537–549.

29.

Kwakernaak

Sivan

. Linear optimal control systems. New York: John Wiley & Sons, 1972.

30.

Yang

X-S

. Chapter 5: genetic algorithms. In: Yang

X-S

(ed.) Nature-inspired optimization algorithms. Oxford: Elsevier, 2014, pp.77–87.

31.

Johar

Azmin

Suaidi

et al . A review of genetic algorithms and parallel genetic algorithms on graphics processing unit (GPU). In: Proceedings of the 2013 IEEE international conference on control system, computing and engineering (ICCSCE), Mindeb, Malaysia, 29 November–1 December 2013, pp.264–269. New York: IEEE.

32.

Tabassum

Mathew

. A genetic algorithm analysis towards optimization solutions. Int J Digit Inform Wire Commun 2014; 4(1): 124–142.

33.

Kantardzic

. Data mining: concepts, models, methods, and algorithms. New York: John Wiley & Sons, 2011.

34.

Gill

Cohen

. Foundations of informing science: 1999-2008. Santa Rosa, CA: Informing Science Institute, 2009.

35.

Khan

Sahai

. A comparison of BA, GA, PSO, BP and LM for training feed forward neural networks in e-learning context. Int J Intell Syst Appl 2012; 4(7): 23.

36.

Zaychik

Cardullo

. Nonlinear motion cueing algorithm: filtering at pilot station and development of the nonlinear optimal filters for pitch and roll, 2012, https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20120008933.pdf

37.

Khosravi

Nahavandi

Creighton

. A prediction interval-based approach to determine optimal structures of neural network meta models. Expert Syst Appl 2010; 37(3): 2377–2387.

38.

Khosravi

Nahavandi

Creighton

. Prediction interval construction and optimization for adaptive neurofuzzy inference systems. IEEE T Fuzzy Syst 2011; 19(5): 983–988.