基于数据驱动的平面铰接多刚体系统动力学建模  

Dynamic Modeling of Planar Articulated Multi-Rigid Body Systems Based on Data Drive

作　　者：艾之淏 王康昊 刘晓峰 Ai Zhihao;Wang Kanghao;Liu Xiaofeng(School of Ocean and Civil Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]上海交通大学船舶海洋与建筑工程学院,上海200240

出　　处：《动力学与控制学报》2025年第2期55-63,共9页Journal of Dynamics and Control

摘　　要：动力学模型是模拟物理系统的一种有效工具,能够帮助人们深入理解物理系统的运行规律,为物理系统的预测、优化设计以及控制系统的开发提供理论支持.近年来,基于数据驱动的动力学建模方法引起了学界的广泛关注.已有研究虽然取得了一定成果,但仍存在一些不足之处.本文深入研究了基于数据驱动的平面铰接多刚体系统动力学建模问题,并在拉格朗日神经网络(LNN)的基础上提出了一种改进的数据驱动建模方法——拓扑拉格朗日神经网络(TLNN).相较于LNN,TLNN通过嵌入多体系统的拓扑信息,实现了神经网络学习性能的提高.预测结果显示,使用相同训练数据集,相较LNN、哈密顿神经网络(HNN)以及神经常微分方程(NODE)三种数据驱动建模方法,TLNN可以建立精度更高的铰接多刚体动力学代理模型.另外,本文对数据驱动建模过程所涉及广义坐标选择问题进行讨论.训练和预测结果均显示,相较于选择关节相对角度进行数据驱动建模,采用刚体绝对姿态角进行建模可以获得精度更高的动力学代理模型.Dynamic models serve as effective tools for simulating physical systems,facilitating a deeper understanding of the operational principles governing systems.They provide theoretical underpinnings for prediction,optimization,and control system development.In recent years,data-driven approaches for dynamic modeling have garnered widespread attention in academia.While significant progress has been made,there remain limitations.This paper delves into the data-driven modeling of planar articulated multibody systems and proposes an improved neural network framework,termed Topological Lagrangian Neural Network(TLNN),building upon the foundation of Lagrangian Neural Networks(LNN).Compared to LNN,TLNN leverages topological information embedded within multibody systems,enhancing the learning performance of neural networks.Prediction results demonstrate that TLNN establishes higher-precision dynamic proxy models for articulated multibody dynamics compared to LNN,Hamiltonian Neural Networks(HNN),and Neural Ordinary Differential Equations(NODE)when trained on the same dataset.Furthermore,this paper discusses the generalized coordinate selection issue in the data-driven modeling process.Both training and prediction results indicate that utilizing rigid body absolute angles for modeling yields dynamic proxy models with higher precision compared to modeling based on joint relative angles in data-driven modeling of articulated multibody systems.

关 键 词：动力学建模 铰接刚体 拓扑拉格朗日神经网络 拉格朗日力学 

分 类 号：O313.3[理学—一般力学与力学基础]