Model Predictive Control based Motion Cueing Algorithm for Driving Simulator

Abstract

Thanks to the emerging integration of algorithms and simulators, recent Driving Simulators (DS) find enormous potential in applications like advanced driver-assistance devices, analysis of driver’s behaviours, research and development of new vehicles and even for entertainment purposes. Driving simulators have been developed to reduce the cost of field studies, allow more flexible control over circumstances and measurements, and safely present hazardous conditions. The major challenge in a driving simulator is to reproduce realistic motions within hardware constraints. Motion Cueing Algorithm (MCA) guarantees a realistic motion perception in the simulator. However, the complex nature of the human perception system makes MCA implementation challenging. The present research aims to improve the performance of driving simulators by proposing and implementing the MCA algorithm as a control problem. The approach is realized using an actual vehicle model integrated with a detailed model of the human vestibular system, which accurately reproduces the driver’s perception. These perception motion signals are compared with simulated ones. A 2-DOF stabilized platform model is used to test the results from the two proposed control strategies, Proportional Integrator and Derivative (PID) and Model Predictive Control (MPC).

Keywords

Driving simulator / motion cueing algorithm / control theory / model predictive control / PID

References

[1] Abadi A S S. A novel control system for synchronizing chaotic systems in the presence of communication channel time delay; case study of Genesio-Tesi and Coullet systems. Nonlinear Analysis: Hybrid Systems, 2023, 50: 101408.

[2] Abadi A S S, Ordys A, Pierscionek B (2023). Controlling a teleoperated robotic eye surgical system under a communication channel’s unknown time delay. 27th International Conference on Methods and Models in Automation and Robotics: 211–215.

[3] Abadi A S S, Ordys A, Pierscionek B. Novel off-line self-tuning controller with guaranteed stability. International Journal of Automotive Technology, 2023, 24(3): 851-862.

[4] Allerton D J. The impact of flight simulation in aerospace. The Aeronautical Journal, 2010, 114(1162): 747-756.

[5] Asadi H, Mohamed S, Lim C P, Nahavandi S. Robust optimal motion cueing algorithm based on the linear quadratic regulator method and a genetic algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016, 47(2): 238-254.

[6] Asadi H, Mohamed S, Lim C P, Nahavandi S. A review on otolith models in human perception. Behavioural Brain Research, 2016, 309: 67-76.

[7] Asadi H, Bellmann T, Qazani M C, Mohamed S, Lim C P, Nahavandi S. A novel decoupled model predictive control-based motion cueing algorithm for driving simulators. IEEE Transactions on Vehicular Technology, 2023, 99: 1-12.

[8] Augusto B, Loureiro R (2009). Motion cueing in the Chalmers driving simulator: A model predictive control approach. Master Thesis.

[9] Biemelt P, Link C, Gausemeier S, Trächtler A. A model-based online reference prediction strategy for model predictive motion cueing algorithms. IFAC-PapersOnLine, 2020, 53(2): 6082-6088.

[10] Blissing B, Bruzelius F, Eriksson O. The effects on driving behaviour when using a head-mounted display in a dynamic driving simulator. ACM Transactions on Applied Perception, 2022, 19(1): 1-18.

[11] Brown C (2023). A nonlinear extension to classical filters for washout miscue prevention. Driving Simulation Conference 2023 Europe VR.

[12] Bruschetta M, Maran F, Beghi A. A fast implementation of MPC-based motion cueing algorithms for midsize road vehicle motion simulators. Vehicle System Dynamics, 2017, 55(6): 802-826.

[13] Casas S, Olanda R, Dey N. Motion cueing algorithms: A review: Algorithms, evaluation and tuning. International Journal of Virtual and Augmented Reality (IJVAR), 2017, 1(1): 90-106.

[14] Chadha A, Jain V, Lazcano A M R, Shyrokau B (2023). Computationally-efficient motion cueing algorithm via model predictive control. IEEE International Conference on Mechatronics: 1–6.

[15] Cleij, et al. Comparison between filter-and optimization-based motion cueing algorithms for driving simulation. Transportation Research Part F: Traffic Psychology and Behaviour, 2019, 61: 53-68.

[16] Colombet F, Dagdelen M, Reymond G, Pere C, Merienne F, Kemeny A (2008). Motion cueing: What is the impact on the driver’s behaviour. Proceedings of the Driving Simulation Conference: 171–181.

[17] Iqbal S (2011). Robust smooth model-free control methodologies for industrial applications. Doctoral Dissertation, MAJU University.

[18] Karthaus M, Wascher E, Falkenstein M, Getzmann S. The ability of young, middle-aged and older drivers to inhibit visual and auditory distraction in a driving simulator task. Transportation Research Part F: Traffic Psychology and Behaviour, 2020, 68: 272-284.

[19] Khusro Y R, Zheng Y, Grottoli M, Shyrokau B. MPC-based motion-cueing algorithm for a 6-DOF driving simulator with actuator constraints. Vehicles, 2020, 2(4): 625-647.

[20] Koyuncu A B, Erçelik E, Comulada-Simpson E, Venrooij J, Kaboli M, Knoll A (2020). A novel approach to neural network-based motion cueing algorithm for a driving simulator. IEEE Intelligent Vehicles Symposium (IV) 2118–2125.

[21] Kraft E, He P, Rinderknecht S. Evaluation of drive-off procedures in dynamic driving simulators: Multi-sensory cues, interaction and sense of presence. Transportation Research Part F: Traffic Psychology and Behaviour, 2023, 95: 129-142.

[22] Mohammadi A, Asadi H, Mohamed S, Nelson K, Nahavandi S (2016). MPC-based motion cueing algorithm with short prediction horizon using exponential weighting. In 2016 IEEE International Conference on Systems, Man, and Cybernetics: 000521–000526.

[23] Nahon M A, Reid L D. Simulator motion-drive algorithms: A designer’s perspective. Journal of Guidance, Control, and Dynamics, 1990, 13(2): 356-362.

[24] Pham D A, Pham T N, Nguyen D T (2023). Novel model predictive control-based motion cueing algorithm for compensating centrifugal acceleration in KUKA robocoaster-based driving simulators. Science Progress 106(4).DOI: https://doi.org/10.1177/00368504231204759.

[25] Prakash R, Dheer D K. Evolutionary algorithms-based model predictive control for vehicle lateral and roll motion control. Arabian Journal for Science and Engineering, 2023, 48(5): 6857-6871.

[26] Pretto P, Venrooij J, Nesti A, Bülthoff H H (2015). Perception-based motion cueing: A cybernetics approach to motion simulation. Recent Progress in Brain and Cognitive Engineering: 131–152.

[27] 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 Transactions on Systems, Man, and Cybernetics: Systems, 2019, 51(10): 6096-6110.

[28] Qazani M R C, Asadi H, Nahavandi S. High-fidelity hexarot simulation-based motion platform using fuzzy incremental controller and model predictive control-based motion cueing algorithm. IEEE Systems Journal, 2019, 14(4): 5073-5083.

[29] Qazani M R C, Asadi H, Nahavandi S. A new gantry-tau-based mechanism using spherical wrist and model predictive control-based motion cueing algorithm. Robotica, 2020, 38(8): 1359-1380.

[30] Qazani M R C, Asadi H, Mohamed S, Lim C P, Nahavandi S. A time-varying weight MPC-based motion cueing algorithm for motion simulation platform. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(8): 11767-11778.

[31] Qazani M R C, Asadi H, Chen Y, Abdar M, Karkoub M, Mohamed S, Nahavandi S (2023). An optimal nonlinear model predictive control-based motion cueing algorithm using cascade optimization and human interaction. IEEE Transactions on Intelligent Transportation Systems.

[32] Reymond G, Kemeny A. Motion cueing in the Renault driving simulator. Vehicle System Dynamics, 2000, 34(4): 249-259.

[33] Sharma A, Ikbal M S, Cuong D T, Zoppi M. A sliding mode-based approach to motion cueing for virtual reality gaming using motion simulators. Virtual Reality, 2021, 25: 95-106.

[34] Telban R, Cardullo F (2001). An integrated model of human motion perception with visual-vestibular interaction. AIAA Modeling and Simulation Technologies Conference and Exhibit. DOI: https://doi.org/10.2514/6.2001-4249.

[35] Venrooij J, Pretto P, Katliar M, Nooij S A, Nesti A, Lächele M, Bülthoff H H (2015). Perception-based motion cueing: Validation in driving simulation. Proceeding of Driving Simulator Conference: 153–161.

[36] Weinberg G, Harsham B (2009). Developing a low-cost driving simulator for the evaluation of in-vehicle technologies. Proceedings of the 1st International Conference on Automotive User Interfaces and Interactive Vehicular Applications, USA.