高抗干扰性的无力传感器工业机器人外力估计方法

doi:10.3901/JME.260091

机械工程学报 ›› 2026, Vol. 62 ›› Issue (3): 366-383.doi: 10.3901/JME.260091

• 机器人及机构学 • 上一篇

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高抗干扰性的无力传感器工业机器人外力估计方法

郭万金^1,2,3, 利乾辉¹, 徐明坤¹, 侯旭栋¹, 刘孝恒¹, 曹雏清^2,4, 赵立军^2,5

1. 长安大学道路施工技术与装备教育部重点实验室西安 710064;
2. 长三角哈特机器人产业技术研究院芜湖 241007;
3. 埃夫特智能机器人股份有限公司芜湖 241060;
4. 安徽工程大学计算机与信息学院芜湖 241000;
5. 哈尔滨工业大学机器人研究所哈尔滨 150001

修回日期:2025-05-19 接受日期:2025-10-14 发布日期:2026-03-25
作者简介:郭万金(通信作者),男,1983年出生,博士,副教授,博士研究生导师。主要研究方向为工业机器人打磨与主动柔顺控制。E-mail:guowanjin@chd.edu.cn

External Force Estimation Method for Force Sensorless Industrial Robots with High Anti-interference

GUO Wanjin^1,2,3, LI Qianhui¹, XU Mingkun¹, HOU Xudong¹, LIU Xiaoheng¹, CAO Chuqing^2,4, ZHAO Lijun^2,5

1. Key Laboratory of Road Construction Technology and Equipment, Ministry of Education, Chang'an University, Xi'an 710064;
2. Yangtze River Delta HIT Robot Technology Research Institute, Wuhu 241007;
3. EFORT Intelligent Robot, Co., Ltd., Wuhu 241060;
4. School of Computer and Information, Anhui Polytechnic University, Wuhu 241000;
5. Robotics Institute, Harbin Institute of Technology, Harbin 150001

Revised:2025-05-19 Accepted:2025-10-14 Published:2026-03-25
Supported by:
国家自然科学基金面上项目(52275005)、陕西省自然科学基础研究计划资助(2025JC-QYXQ-027,2025JC-YBMS-619)、中央高校基本科研业务费专项资金(300102253201)、中国博士后科学基金(2024M760002)、安徽省机器视觉检测与感知重点实验室开放基金(KLMVI-2025-HIT-06)和长安大学高等教育教学改革研究项目(BZ202521)资助项目。

摘要/Abstract

摘要： 为了解决工业机器人外力估计依赖于精确的动力学模型、抗干扰性差、精度低和可解释性不足的问题，提出了一种高抗干扰性的无力传感器工业机器人外力估计方法，实现了高抗干扰性机器人外力估计。首先，考虑关节摩擦力矩和外力影响建立了3T2R构型五自由度工业机器人动力学模型，并引入Stribeck摩擦-速度模型描述摩擦力矩的非线性特征；其次，利用自适应率增益参数优化的自适应超螺旋滑模广义动量观测器估计机器人关节外力矩，以提高关节外力矩的估计精度，降低对精确动力学模型的依赖，增强关节外力矩估计方法的可解释性；再次，利用雅可比矩阵建立机器人关节外力矩与机器人外力的映射关系，实现机器人外力估计；将所提方法与自适应超螺旋滑模、超螺旋滑模和一阶广义动量观测器方法进行对比实验分析，以关节1为例，所提方法与真实关节外力矩的平均绝对误差减小了12.9%、39.9%和57.9%，并利用所建立映射关系获得机器人外力估计，所提方法具有更高的估计精度，验证了所提方法的有效性；最后，施加同一随机扰动后将所提方法与一阶广义动量观测器方法进行对比，所提方法与真实关节外力矩均方根误差增大了1.5%，而一阶广义动量观测器方法增大了43.3%，验证了所提方法具有更强的抗扰性。[①]

关键词: 工业机器人, 外力估计, 摩擦力矩模型, 广义动量观测器, 超螺旋滑模

Abstract: To address the issues of external force estimation for industrial robots—namely, its dependence on accurate dynamic models, poor disturbance rejection, low estimation accuracy, and insufficient interpretability—an external force estimation method for force sensorless industrial robots with high anti-interference is proposed. Firstly, a dynamic model of a 5-degree-of-freedom industrial robot with a 3T2R configuration is established, considering the effects of joint friction torque and external forces. The nonlinear characteristics of joint friction are modeled using the Stribeck friction–velocity model. Secondly, an adaptive super-twisting sliding mode generalized momentum observer, with an adaptively tuned rate gain parameter, is then employed to estimate joint external torques. This approach improved estimation accuracy, reduced reliance on precise dynamic models, and enhanced interpretability. Thirdly, a mapping relationship between joint external torques and external forces is constructed using the Jacobian matrix, enabling external force estimation. Fourthly, comparative experiments are conducted against the adaptive super-twisting sliding mode observer, the super-twisting sliding mode observer, and the first-order generalized momentum observer. Taking Joint 1 as an example, the proposed method reduced the mean absolute error of joint external torque estimation by 12.9%, 39.9%, and 57.9%, respectively. Based on the established mapping, external forces are further estimated, demonstrating higher accuracy and validating the effectiveness of the proposed method. Finally, under the application of the same random disturbance, the proposed method is compared with the first-order generalized momentum observer. The root mean square error of the estimated joint external torque increased by only 1.5% for the proposed method, whereas it increased by 43.3% for the first-order method, verifying the superior disturbance rejection capability of the proposed method.

Key words: industrial robots, external force estimation, friction torque model, generalized momentum observer, super-twisting sliding mode

中图分类号:

TP242

郭万金, 利乾辉, 徐明坤, 侯旭栋, 刘孝恒, 曹雏清, 赵立军. 高抗干扰性的无力传感器工业机器人外力估计方法[J]. 机械工程学报, 2026, 62(3): 366-383.

GUO Wanjin, LI Qianhui, XU Mingkun, HOU Xudong, LIU Xiaoheng, CAO Chuqing, ZHAO Lijun. External Force Estimation Method for Force Sensorless Industrial Robots with High Anti-interference[J]. Journal of Mechanical Engineering, 2026, 62(3): 366-383.

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高抗干扰性的无力传感器工业机器人外力估计方法

External Force Estimation Method for Force Sensorless Industrial Robots with High Anti-interference

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