Chalak Qazani, Mohammad Reza, Asadi, Houshyar, Zhang, Li, Tabarsinezhad, Farzin, Mohamed, Shady, Lim, Chee Peng, and Nahavandi, Saeid (2022) A New Prepositioning Technique of a Motion Simulator Platform Using Nonlinear Model Predictive Control and Recurrent Neural Network. IEEE Transactions on Intelligent Transportation Systems, 23 (12). pp. 23268-23277.

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Abstract

The motion cueing algorithm (MCA) is the main algorithm in motion simulators in charge of generating vehicle motions within the platform’s constraints. The classical washout filter is one of the popular types of MCA, which is used in air and land vehicle motion simulators. The fixed home position of the simulator platform is always cogitated in the MCA to washout the motion simulator after generating each motion. Unfortunately, considering the fixed home position reduces the efficient consumption of the workspace in the linear directions. The linear motion of the motion simulator is due to the production of the high-pass frequency part of the motion scenarios. Prepositioning is used to tackle this assumption by varying the home position rather than the fixed position. The linear motion limitations of the motion simulator can virtually be enlarged using the prepositioning method. The efficient regeneration of the high-pass motion cues using a new propositioning technique is the main goal of this study to increase the motion realism of the simulator and remove any false motion cues due to the platform limitations. The proposed model utilised the recurrent neural network (RNN) to estimate the motion scenario along the prediction horizon. The nonlinear model predictive control (MPC) uses the estimated motion signals to extract the best optimal off-centre position of the motion simulator platform. The newly developed prepositioning technique is developed in the simulation environment of MATLAB to validate the proposed technique in terms of efficiency and applicability. The outcomes prove the capability of the proposed technique against the recently developed prepositioning technique using fuzzy logic and RNN.

Item ID:	86735
Item Type:	Article (Research - C1)
ISSN:	1558-0016
Copyright Information:	© 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
Date Deposited:	15 Oct 2025 03:16
FoR Codes:	40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400705 Control engineering @ 60% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461104 Neural networks @ 10% 40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400706 Field robotics @ 30%
SEO Codes:	27 TRANSPORT > 2799 Other transport > 279999 Other transport not elsewhere classified @ 40% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 60%
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