Chalak Qazani, Mohamad Reza, Asadi, Houshyar, Arogbonlo, Adetokunbo, Rahimzadeh, Ghazal, Mohamed, Shady, Pedrammehr, Siamak, Peng Lim, Chee, and Nahavandi, Saeid (2021) Whale Optimization Algorithm for Weight Tuning of a Model Predictive Control-Based Motion Cueing Algorithm. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. pp. 1042-1048. From: SMC 2021: IEEE International Conference on Systems, Man, and Cybernetics, 17-20 October 2021, Melbourne, VIC, Australia.

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Abstract

The purpose of the motion cueing algorithm is to reproduce the motion sensation for the drivers considering the physical limitations of this platform. Newly, the model predictive control-based methods have been used in motion cueing algorithms. This control respects the constraints and considers the future dynamics of the model for finding the optimum solution to the problem. However, the tuning of the weights for model predictive control is incredibly challenging to reduce the motion sensation errors. In this paper, a whale optimisation algorithm is used to gain the optimised weights of the model predictive control. The weights are optimized to reduce the cost function which is defined based on the motion inputs, input rates, and outputs. The recalculated weights via the whale optimisation algorithm should consider the limitations of the applications such as maximum tolerated error of motion sensation via the motion platform user, maximum linear and angular displacements, and maximum linear velocity. The proposed method is simulated seven times to demonstrate the accuracy and repeatability of the algorithm. The results show that the whale optimisation algorithm reaches the best solution quickly with minimised motion sensation error compared with the genetic algorithm.

Item ID:	87042
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-6654-4207-7
Copyright Information:	© 2021 IEEE
Date Deposited:	04 Sep 2025 01:59
FoR Codes:	40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400705 Control engineering @ 50% 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460203 Evolutionary computation @ 30% 40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400706 Field robotics @ 20%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 100%
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