Chalak Qazani, Mohamad Reza, Asadi, Houshyar, Chen, Yutao, Abdar, Moloud, Karkoub, Mansour, Mohamed, Shady, Lim, Chee Peng, and Nahavandi, Saeid (2023) An optimal nonlinear model predictive control-based motion cueing algorithm using cascade optimization and human interaction. IEEE Transactions on Intelligent Transportation Systems, 24 (9). pp. 9191-9202.

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Abstract

Nonlinear model predictive control has been used in motion cueing algorithms recently to consider the nonlinear dynamics model of the system. The entire motion cueing algorithm indexes, including the physical and dynamical constraints of the actuators and physical constraints of passive joints, can be controlled with precision using nonlinear model predictive control. However, several weighting parameters in the nonlinear model predictive control-based motion cueing algorithm (including driving sensation, motion description of the actuators, and passive joints) require proper and laborious tuning to attain an optimal design structure. In this work, the optimal weighting parameters of a nonlinear predictive control-based motion cueing algorithm model are calculated using cascade optimisation and human interaction. A cascade optimisation method consisting of a particle swarm optimisation and genetic algorithm is designed to identify the best weighting parameters compared to those from one optimiser. In addition, the human decision-making units are added to the two-level cascade optimiser to determine the best solution from a Pareto front. The proposed cascade optimiser decreases the run-time with better extraction of the optimal weighting parameters to increase the motion fidelity compared to a single optimiser. It should be noted that the proposed methodology is applied along longitudinal channel. While the same methodology can be applied along lateral, heave and yaw channels for further evaluation of the proposed method. The proposed model is simulated utilising the MATLAB software and the results prove the efficiency of the newly proposed model compared to those from the previous single optimiser in reproducing more accurate motion signals with better usage of the driving motion platform workspace.

Item ID:	86728
Item Type:	Article (Research - C1)
ISSN:	1558-0016
Copyright Information:	© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
Date Deposited:	20 Aug 2025 23:57
FoR Codes:	40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400799 Control engineering, mechatronics and robotics not elsewhere classified @ 80% 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified @ 20%
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