Chalak Qazani, Mohamad Reza, Asadi, Houshyar, Mohamed, Shady, Alqumsan, Ahmad Abu, Rahimzadeh, Ghazal, Lim, Chee Peng, and Nahavandi, Saeid (2021) An MPC-based Motion Cueing Algorithm Using Washout Speed and Grey Wolf Optimizer. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. pp. 1627-1633. From: SMC 2021: IEEE International Conference on Systems, Man, and Cybernetics, 17-20 October 2021, Melbourne, VIC, Australia.

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Abstract

The motion simulator platform can be used in many sectors, including transportation, aviation, and education. The motion cueing algorithm (MCA) is the main component of the motion simulator with the responsibility of motion cues regeneration while respecting the motion simulator’s joint limitations. Recently, a model predictive control (MPC) method has been introduced in the MCA, which is able to extract an optimum input signal within the model constraints. The washout speed of the end-effector using the existing MPC-based MCA model is not considered because the integral of linear displacement of the platform is omitted as an output. As a result, the motion simulator platform returns to the neutral position without the consideration of the motion behavior. In this study, the integral of the end-effector linear displacement is consider inside the MPC-based MCA model to select the best washout speed of the end-effector. Moreover, a grey wolf optimizer is utilized to identify the best MPC weighting indexes, in order to increase the model efficiency for both existing and proposed models. The proposed method outperforms the MPC-based MCA model in producing better regeneration of the motion cues. It yields a higher correlation coefficient and a lower root means square error between the motion sensation signal pertaining to the real vehicle and motion simulator platform users.

Item ID:	87029
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-6654-4207-7
Copyright Information:	© 2021 IEEE.
Date Deposited:	04 Sep 2025 01:11
FoR Codes:	40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400705 Control engineering @ 60% 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460203 Evolutionary computation @ 40%
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