基于改进Stribeck摩擦的协作机器人动力学参数辨识

doi:10.3901/JME.260234

摘要/Abstract

摘要： 精确的动力学模型对于协作机器人的控制和安全在人机交互任务中至关重要，其研究核心为动力学模型参数辨识。动力学模型中的摩擦项建模不精确会导致机器人控制稳定性和运动精度较差，针对此问题提出一种改进Stribeck摩擦模型，其添加加速度项和速度项以更好地拟合动力学模型中的非线性。首先，利用拉格朗日法建立带关节摩擦力项的机器人动力学模型，并将其分为线性和非线性部分；通过六自由协作机器人平台，以三阶傅里叶级数信号作为关节轨迹输入，并对采集的数据处理并滤波，建立参数辨识的实验数据集；最后利用该数据集辨识动力学模型参数，即利用基于迭代加权的约束最小二乘法辨识线性部分；利用基于密度的带有噪声的空间聚类法(DBSCAN)和灰狼-布谷鸟混合优化算法(GWO-CS)辨识非线性部分。实验结果表明，改进Stribeck摩擦模型能准确拟合关节摩擦的非线性项，相比传统库仑-粘滞摩擦模型和标准Stribeck模型，关节力矩的均方根误差(RMSE)大幅降低，有效地提高协作臂动力学模型的参数辨识精度。

关键词: 协作机器人, 参数辨识, Stribeck摩擦, 优化算法

Abstract: Accurate dynamic models are crucial for the control and safety of collaborative robots in human–robot interaction tasks, and the core of this research is dynamic model parameter identification. Inaccurate modelling of friction terms in dynamic models can lead to poor robot control, stability, and motion accuracy. To address this issue, a modified Stribeck friction model is proposed, which adds acceleration and velocity terms to better fit the nonlinearities in the dynamic model. First, a robot dynamic model with joint friction terms is established using the Lagrangian method, and divided into linear and nonlinear components. Using a six-degree-of-freedom collaborative robot platform, third-order Fourier series signals are used as joint trajectory inputs. The collected data is processed and filtered to establish an experimental dataset for parameter identification. Finally, this dataset is used to identify the dynamic model parameters. The linear component is identified using the constrained iteratively reweighted least squares method, while the nonlinear component is identified using the density-based spatial clustering of applications with noise (DBSCAN) and the grey wolf optimization-cuckoo search hybrid optimization algorithm (GWO-CS). Experimental results show that the improved Stribeck friction model can accurately fit the nonlinear terms of joint friction. Compared with the traditional Coulomb–viscous friction model and the standard Stribeck model, the root mean square error (RMSE) of the joint torque is greatly reduced, effectively improving the parameter identification accuracy of the collaborative arm dynamics model.

Key words: collaborative robot, parameter identification, Stribeck friction, optimization algorithm

中图分类号:

TP242

张涛, 何家豪, 施永平, 赵萍, 汪键, 王伟. 基于改进Stribeck摩擦的协作机器人动力学参数辨识[J]. 机械工程学报, 2026, 62(5): 151-167.

ZHANG Tao, HE Jiahao, SHI Yongping, ZHAO Ping, WANG Jian, WANG Wei. Dynamic Parameter Identification of Collaborative Robot Based on Improved Stribeck Friction Model[J]. Journal of Mechanical Engineering, 2026, 62(5): 151-167.

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链接本文: http://www.cjmenet.com.cn/CN/10.3901/JME.260234

http://www.cjmenet.com.cn/CN/Y2026/V62/I5/151

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