Arango, Camilo Gonzalez, Asadi, Houshyar, Chalak Qazani, Mohamad Reza, Mohamed, Shady, and Nahavandi, Saeid (2022) Automatic Tuning of Adaptive Gradient Descent Based Motion Cueing Algorithm Using Particle Swarm Optimisation. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. pp. 85-92. From: SMC 2022: IEEE International Conference on Systems, Man, and Cybernetics, 9-12 October 2022, Prague, Czech Republic.

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Abstract

A Motion Cueing Algorithm (MCA) is an algorithm that transforms the movement of a simulated vehicle into movement that can be reproduced with a Motion Simulator (MS) while respecting its physical constraints. Crucially, MCAs aim to provide a realistic driving experience to simulator users. Adaptive MCAs are a type of MCA that is flexible, computationally light and designed to adjust behaviour based on the current MS state. However, adaptive MCAs require extensive manual tuning which is difficult, time consuming and a sub-optimal process. This paper presents an optimisation-based method using Particle Swarm Optimisation (PSO) for automatically tuning the free parameters of the Adaptive Gradient Descent-based MCA (AGDA) while accounting for MS physical constraints and motion fidelity. The cost function of the tuning routine considers the RMSE, correlation coefficient (CC) and error oscillation of the motion sensation signals of the MS driver with respect to those of the simulated vehicle driver. The displacement, velocity and acceleration of the MS are also considered. The proposed method was implemented using MATLAB and Simulink and the effectiveness of the approach was tested with a Rigs of Rods simulation of a ground vehicle. Compared to the existing manually tuned AGDA, the optimally tuned AGDA obtained with the proposed method performs 32.6% and 23.7% better in terms of RMSE and CC of the motion sensation signals, respectively. The observed performance improvement and moderate computational load of the AGDA renew its relevance in the context of modern MCAs.

Item ID:	87059
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-6654-5258-8
Copyright Information:	© 2022 IEEE.
Date Deposited:	04 Sep 2025 02:56
FoR Codes:	40 ENGINEERING > 4007 Control engineering, mechatronics and robotics > 400705 Control engineering @ 70% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 30%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 100%
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