Purpose

The purpose of this paper is to propose a new joint friction model, which can accurately model the real friction, especially in cases with sudden changes in the motion direction. The identification and sensor-less control algorithm are investigated to verify the validity of this model.

Design/methodology/approach

The proposed friction model is nonlinear and it considers the angular displacement and angular velocity of the joint as a secondary compensation for identification. In the present study, the authors design a pipeline – including a manually designed excitation trajectory, a weighted least squares algorithm for identifying the dynamic parameters and a hand guiding controller for the arm’s direct teaching.

Findings

Compared with the conventional joint friction model, the proposed method can effectively predict friction factors during the dynamic motion of the arm. Then friction parameters are quantitatively obtained and compared with the proposed friction model and the conventional friction model indirectly. It is found that the average root mean square error of predicted six joints in the proposed method decreases by more than 54%. The arm’s force control with the full torque using the estimated dynamic parameters is qualitatively studied. It is concluded that a light-weight industrial robot can be dragged smoothly by the hand guiding.

Practical implications

In the present study, a systematic pipeline is proposed for identifying and controlling an industrial arm. The whole procedure has been verified in a commercial six DOF industrial arm. Based on the conducted experiment, it is found that the proposed approach is more accurate in comparison with conventional methods. A hand-guiding demo also illustrates that the proposed approach can provide the industrial arm with the full torque compensation. This essential functionality is widely required in many industrial arms such as kinaesthetic teaching.

Originality/value

First, a new friction model is proposed. Based on this model, identifying the dynamic parameter is carried out to obtain a set of model parameters of an industrial arm. Finally, a smooth hand guiding control is demonstrated based on the proposed dynamic model.

中文翻译：

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用于工业机器人参数识别和无传感器手引导的新型关节摩擦模型

目的

本文的目的是提出一种新的关节摩擦模型，该模型可以准确地模拟实际摩擦，尤其是在运动方向突然变化的情况下。研究了识别和无传感器控制算法，以验证该模型的有效性。

设计/方法/方法

所提出的摩擦模型是非线性的，并且将关节的角位移和角速度视为识别的辅助补偿。在本研究中，作者设计了一条管线-包括手动设计的激励轨迹，用于识别动态参数的加权最小二乘算法以及用于手臂直接示教的手动引导控制器。

发现

与传统的关节摩擦模型相比，该方法可以有效地预测手臂动态运动过程中的摩擦系数。然后定量获得摩擦参数，并将其间接与建议的摩擦模型和常规摩擦模型进行比较。结果表明，该方法预测的六个关节的平均均方根误差降低了54％以上。定性地研究了使用估计的动态参数在全扭矩下进行的手臂力控制。结论是，轻巧的工业机器人可以通过手动引导平稳地拖动。

实际影响

在本研究中，提出了一种用于识别和控制工业部门的系统管道。整个程序已在商业六自由度工业部门中得到验证。根据进行的实验，发现所提出的方法与常规方法相比更加准确。手动演示还说明了所提出的方法可以为工业机械臂提供完整的扭矩补偿。在诸如动觉教学之类的许多工业领域中，广泛要求此基本功能。

创意/价值

首先，提出了一种新的摩擦模型。基于该模型，进行动态参数的识别以获得一组工业臂的模型参数。最后，基于所提出的动态模型证明了平滑的手引导控制。