基于神经网络的无人车动力学建模方法

doi:10.16182/j.issn1004731x.joss.24-0935

摘要/Abstract

摘要：

针对无人车在复杂陆域环境下轮胎与软路面之间动力学特性试验数据获取成本高、数值计算速度慢的问题，提出一种基于神经网络的无人车动力学模型建模 方法，利用轮胎-路面接触离散元模拟及试验数据构建轮-地接触动力学模型，生成了陆域环境不同材质下轮胎接触力试验数据集，应用神经网络对该数据集进行回归学习，构建了非线性神经网络轮胎模型，搭建了无人车三自由度动力学模型，并基于该模型仿真生成了具有物理意义及边界的数据集。仿真实验结果表明：该模型可以实现无人车动力学高精度和高效率仿真，满足模拟仿真环境下大规模无人车高精度快速解算需求，实现细粒度无人车轨迹跟踪控制。

关键词: 神经网络, 深度学习, 汽车动力学建模, 接触离散元建模, 轮胎摩擦建模

Abstract:

To address the challenges of high data acquisition costs of test data on dynamic characteristics between tires and soft terrain and low speed of numerical calculation for unmanned vehicles in complex terrestrial environments, a modeling method of unmanned vehicle dynamics based on a neural network was proposed. Tire-terrain contact dynamics models were built by using discrete element method (DEM) simulations for tire-terrain contact and experimental data, thereby creating a dataset of tire contact forces for various tire materials in terrestrial environments. The neural network was applied to regressively learn the dataset, and a nonlinear neural network tire model was constructed. Subsequently, a three-degree-of-freedom dynamics model of unmanned vehicles was constructed, and a physically meaningful and bounded dataset was generated. The results demonstrate that the model can achieve high-precision and efficient simulation of unmanned vehicle dynamics, fulfill the requirements for large-scale, high-precision, and fast calculation in simulated environments, and achieve fine-grained trajectory tracking control of unmanned vehicles.

Key words: neural network, deep learning, automotive dynamics modeling, discrete element modeling of contact, tire friction modeling

中图分类号:

TP18

王军,刘敏,张啸川等 . 基于神经网络的无人车动力学建模方法[J]. 系统仿真学报, 2026, 38(4): 932-947.

Wang Jun,Liu Min,Zhang Xiaochuan,et al . Modelling Method of Unmanned Vehicle Dynamics Based on Neural Network[J]. Journal of System Simulation, 2026, 38(4): 932-947.

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参数	沙土颗粒	粘土颗粒
形状	球形	球形
半径/mm	2	2
密度/(kg/m³)	2 100	1 550
剪切模量/MPa	83	16.5
泊松比	0.25	0.25
颗粒-颗粒恢复系数	0.48	0.58
颗粒-颗粒静摩擦系数	0.51	1.21
颗粒-颗粒动摩擦系数	0.71	0.13
颗粒-几何体恢复系数	0.5	0.48
颗粒-几何体静摩擦系数	0.5	0.55
颗粒-几何体动摩擦系数	0.3	0.37
含水量/%	15	55
变形模量	0.7	0.4
内聚模量/(kN/m ⁿ⁺¹)	5.27	16.03
摩擦模量/(kN/m ⁿ⁺¹)	1 515.04	126.53
粘聚力/kPa	1.72	2.07
内摩擦角/(°)	34	10
厚度/mm	300	300

输出状态量	残差神经网络	前馈神经网络	循环神经网络
横摆角速度	0.999 998	0.998 507	0.841 569
纵向速度	0.999 978	0.512 992	0.187 719
侧向速度	0.999 991	0.998 709	0.706 560
纵向加速度	0.999 991	0.996 608	0.177 136
侧向加速度	0.999 998	0.999 840	0.925 140