Dynamic Modeling of Robotic Manipulator via an Augmented Deep Lagrangian Network  

作　　者：Shuangshuang Wu Zhiming Li Wenbai Chen Fuchun Sun 

机构地区：[1]School of Automation,Beijing Information Science and Technology University,Beijing 100192,China [2]Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China

出　　处：《Tsinghua Science and Technology》2024年第5期1604-1614,共11页清华大学学报自然科学版（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.62276028);Major Research Plan of the National Natural Science Foundation of China(No.92267110);Beijing Municipal Natural Science Foundation—Xiaomi Joint Innovation Fund(No.L233006);Beijing Information Science and Technology University School Research Fund(No.2023XJJ12).

摘　　要：Learning the accurate dynamics of robotic systems directly from the trajectory data is currently a prominent research focus.Recent physics-enforced networks,exemplified by Hamiltonian neural networks and Lagrangian neural networks,demonstrate proficiency in modeling ideal physical systems,but face limitations when applied to systems with uncertain non-conservative dynamics due to the inherent constraints of the conservation laws foundation.In this paper,we present a novel augmented deep Lagrangian network,which seamlessly integrates a deep Lagrangian network with a standard deep network.This fusion aims to effectively model uncertainties that surpass the limitations of conventional Lagrangian mechanics.The proposed network is applied to learn inverse dynamics model of two multi-degree manipulators including a 6-dof UR-5 robot and a 7-dof SARCOS manipulator under uncertainties.The experimental results clearly demonstrate that our approach exhibits superior modeling precision and enhanced physical credibility.

关 键 词：deep Lagrangian network nonconservative dynamics multi-degree manipulator inverse dynamic modeling 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP18[自动化与计算机技术—控制科学与工程]