Design and Validation of Motion Cueing Algorithm for Driving Simulator Based on Adaptive Model Predictive Control

doi:10.3901/JME.260211

Abstract

Abstract: The motion cueing algorithm(MCA) is designed to assist users of driving simulators in realistically perceiving a vehicle’s dynamic characteristics. However, under complex road conditions, driving simulators often face challenges such as response delays and reduced trajectory tracking accuracy. To address these issues, a road-preview-based adaptive model predictive control(RPMPC) strategy is proposed. Specifically, a long short-term memory(LSTM) network is employed to construct a dynamic predictive model of the vehicle’s trajectory based on upcoming road conditions. Subsequently, a kinematic model of the driving simulator is established according to fundamental kinematic principles, and, with the aid of adaptive model predictive control and an extended Kalman filter, forward-looking planning of the simulator’s future motion is achieved. Experimental results obtained from a Stewart motion simulation platform indicate that, compared with conventional algorithms, the proposed strategy provides higher-quality motion cues and significantly reduces sensory errors across various road conditions.

Key words: motion cueing algorithm, adaptive model predictive control, trajectory prediction, complex road conditions, driving simulator

CLC Number:

TP249

CHEN Xiafei, JIAO Yiyang, JIANG Xue, MENG Zhaoliang, ZHANG Lijie, LIU Xiaoping. Design and Validation of Motion Cueing Algorithm for Driving Simulator Based on Adaptive Model Predictive Control[J]. Journal of Mechanical Engineering, 2026, 62(10): 298-307.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260211

http://www.cjmenet.com.cn/EN/Y2026/V62/I10/298

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