Al-Serri, Sari, Chalak Qazani, Mohamad Reza, Asadi, Houshyar, Al-Ashmori, Mohammed, Arogbonlo, Adetokunbo, Alqumsan, Ahmad Abu, Alsanwy, Shehab, Mohamed, Shady, Lim, Chee Peng, and Nahavandi, Saeid (2022) Implementation of the Grasshopper Optimisation Algorithm to Optimize Prediction and Control Horizons in Model Predictive Control-based Motion Cueing Algorithm. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. pp. 3317-3323. From: SMC 2022: IEEE International Conference on Systems, Man, and Cybernetics, 9-12 October 2022, Prague, Czech Republic.

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Abstract

Advances in utilisng motion simulators for skill training and related applications have yielded numerous benefits, such as safety, availability, and serviceability, environmentally friendly, and economically beneficial. To give simulator users a sense of realistic feeling of driving, an accurate motion cueing algorithm (MCA) is essential, in order to respect the simulator platform limitation and avoid motion sickness. The use of Model Predictive Control (MPC) in MCA designs leads to respecting the constraints and considering the future dynamic behaviors of the simulator. However, the tuning process of the MPC prediction horizon and control horizon still need to be improved. These horizons are normally selected manually by the designer. Previous studies on meta-heuristic algorithms produce a large prediction horizon with a heavy computational load or a small prediction horizon that sacrifices the stability and accuracy of the simulator system. In this study, the Grasshopper Optimization Algorithm (GOA) is adopted to yield optimal prediction and control horizons in MPC-based MCA models. The results are compared with those from the Butterfly Optimization Algorithm (BOA) and Genetic Algorithm (GA) in terms of sensation error and computation time. The GOA technique depicts the fastest process time to promptly detect proper MPC horizons. It does not affect the simulator's efficiency in utilising the workspace, as evidenced by the correlation coefficient and root mean square error between sensation from a real-world vehicle and the simulator.

Item ID:	87030
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-6654-5258-8
Copyright Information:	© 2022 IEEE
Date Deposited:	03 Sep 2025 01:15
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460203 Evolutionary computation @ 30% 40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400705 Control engineering @ 70%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 100%
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