基于自适应模型预测控制的驾驶模拟器运动提示算法设计与验证

doi:10.3901/JME.260211

机械工程学报 ›› 2026, Vol. 62 ›› Issue (10): 298-307.doi: 10.3901/JME.260211

• 运载工程 • 上一篇

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基于自适应模型预测控制的驾驶模拟器运动提示算法设计与验证

陈夏非¹, 焦翊洋¹, 姜雪¹, 孟昭亮¹, 张立杰^1,2, 刘小平¹

1. 燕山大学河北省重型机械流体动力传输与控制重点实验室秦皇岛 066004;
2. 燕山大学河北省并联机器人与机电系统实验室秦皇岛 066004

收稿日期:2025-05-04 修回日期:2025-11-22 发布日期:2026-07-29
作者简介:陈夏非,男,1994年出生,博士研究生。主要研究方向为并联机器人轨迹规划、轨迹跟踪、状态估计、虚拟现实。E-mail:chenxiafi@163.com;刘小平(通信作者),男,1989年出生,博士,讲师,硕士研究生导师。主要研究方向为液压元件及系统可靠性、故障诊断。E-mail:liu_xp163@163.com
基金资助:
国家自然科学基金资助项目(51875499)。

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

CHEN Xiafei¹, JIAO Yiyang¹, JIANG Xue¹, MENG Zhaoliang¹, ZHANG Lijie^1,2, LIU Xiaoping¹

1. Hebei Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004;
2. Parallel Robot and Mechatronic System of Laboratory of Hebei Province, Yanshan University, Qinhuangdao 066004

Received:2025-05-04 Revised:2025-11-22 Published:2026-07-29

摘要/Abstract

摘要： 运动提示算法(Motion cueing algorithm, MCA)旨在协助驾驶模拟器用户真实地感知车辆的动态特性。然而，在复杂路况条件下，驾驶模拟器往往面临响应滞后及轨迹跟踪精度下降等挑战。为此，提出一种基于路况预览的自适应模型预测控制策略(Road-preview-based adaptive model predictive control, RPMPC)。首先，利用长短期记忆(Long short-term memory, LSTM)网络建立面向车前路况的车辆轨迹动态预测模型；随后，根据运动学原理构建驾驶模拟器的运动学模型，并借助自适应模型预测控制与扩展卡尔曼滤波，实现对驾驶模拟器未来运动的前瞻性规划。在Stewart运动模拟平台上所进行的试验结果表明，相较于传统算法，该策略在不同路况条件下提供了更高质量的运动提示，并显著降低感官误差。

关键词: 运动提示算法, 自适应模型预测控制, 轨迹预测, 复杂路况, 驾驶模拟器

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

中图分类号:

TP249

陈夏非, 焦翊洋, 姜雪, 孟昭亮, 张立杰, 刘小平. 基于自适应模型预测控制的驾驶模拟器运动提示算法设计与验证[J]. 机械工程学报, 2026, 62(10): 298-307.

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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基于自适应模型预测控制的驾驶模拟器运动提示算法设计与验证

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

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