Journal of South China University of Technology(Natural Science) >

2022 , Vol. 50 >Issue 10: 140 - 152

DOI: https://doi.org/10.12141/j.issn.1000-565X.220274

Fluid Power & Electromechanical Control Engineering

Terminal Force Soft Sensing of Hydraulic Manipulator Based on Joint Torque Compensation

  • Gang LI ,
  • Feng LI ,
  • Ruqi DING ,
  • Xueshan MU
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  • Nanchang Key Laboratory of Vehicle Intelligent Equipment and Control/School of Mechatronics and Vehicle，East China Jiaotong University，Nanchang 330013，Jiangxi，China
李刚（1978-），男，博士，副教授，主要从事电液控制及智能装备研究。E-mail:ligang0794@163.com.
周俊杰（1986-），男，博士，副教授，主要从事软体机器人和流体传动与控制研究。

Received date: 2022-05-12

  Online published: 2022-08-30

Supported by

the National Natural Science Foundation of China(52175050);the Regional Innovation and Deve-lopment Joint Funds of the National Natural Science Foundation of China(U21A20124);the Natural Science Foundation of Jiangxi Province(20212ACB214004)

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Abstract

To meet the requirements of the high-precision operation of the hydraulic manipulator in a complex environment, it is necessary to equip it with an accurate force measurement system. Due to the large and complex end-effector contact force against the working environment, the force sensor is vulnerable to damage. Therefore, focu-sing on the accurate measurements of the terminal force with force sensorless, this paper proposed a force soft-sensing method of hydraulic manipulator based on joint torque compensation, taking 3-D of hydraulic manipulator as the research object. A finite Fourier series model was used to design the excitation trajectory, and the recursive least squares method was used to identify the dynamics parameters of the manipulator. The nonlinear friction torque model was used to replace the Coulomb viscous friction model to improve the precision of manipulator dynamics model. A neural network joint torque compensation model was established to reduce the influence of uncertain factors on the precision of the dynamics model. The AMESim/Simulink co-simulation model was built to design the triangular trajectory of the hydraulic manipulator end-effector, which verified the high accuracy of the dynamic model proposed in this research. A constant force load of 500 N and a variable force load of 0~500 N were applied to the horizontal and vertical direction of the end-effector respectively. Under the constant force load, the soft sen-sing accuracy of the end force in the horizontal and vertical directions is 4.84% and 2.79%, respectively. Under the variable force load, the accuracy of the soft sensor can reach 5.73% in the horizontal direction and 4.81% in the vertical direction. By comparing the end force soft sensor accuracy with that before optimization, it is verified that the force soft-sensing model of hydraulic manipulator based on joint torque compensation can effectively improve the accuracy of end-effector contact force.

Key words： hydraulic manipulator; parameter identification; nonlinear friction model; joint torque compensation; terminal force soft sensing

Cite this article

Gang LI , Feng LI , Ruqi DING , Xueshan MU . Terminal Force Soft Sensing of Hydraulic Manipulator Based on Joint Torque Compensation[J]. Journal of South China University of Technology(Natural Science), 2022 , 50(10) : 140 -152 . DOI: 10.12141/j.issn.1000-565X.220274

References

1 WANG X， HUANG J， SONG H. Simultaneous robot-world and hand-eye calibration based on a pair of dual equations ［J］．Measurement，2021，181：109623-1-13.
2 段书用，李昌洛，韩旭，等 ．机械臂动力学分析及关节非线性摩擦模型建立［J］．机械工程学报，2020，56（9）：18-28.
2 DUAN Shuyong， LI Changluo， HAN Xu，et al ．Forward-inverse dynamics analysis of robot arm trajectories and development of a nonlinear friction model for robot joints ［J］.Journal of Mechanical Engineering，2020，56（9）：18-28.
3 ROVEDA L， PIGA D ．Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks ［J］．Autonomous Robots，2021，45（3）：371-388.
4 ZHANG T， SUN H， ZOU Y，et al ．An estimation model of human dynamic arm strength with joint rotation compensation ［J］．Measurement，2021，179：109486-1-10.
5 TRAN C， TRIVEDI M M ．3-D posture and gesture re-cognition for interactivity in smart spaces ［J］．IEEE Transactions on Industrial Informatics，2011，8（1）：178-187.
6 陶东，张强，赵良玉 ．模型不确定的空间机器人无力传感器阻抗控制方法 ［J］．宇航学报，2021，42（6）：766-774.
6 TAO Dong， ZHANG Qiang， ZHAO Liangyu ．Force sensorless impedance control for a space robot with dynamic uncertainty ［J］．Journal of Astronautics，2021，42（6）：766-774.
7 SEBASTIAN G， LI Z， CROCHER V，et al ．Interaction force estimation using extended state observers：An application to impedance-based assistive and rehabilitation robotics ［J］．IEEE Robotics and Automation Letters，2019，4（2）：1156-1161.
8 PHAM T H， CARON S， KHEDDAR A ．Multicontact interaction force sensing from whole-body motion capture ［J］．IEEE Transactions on Industrial Informatics，2017，14（6）：2343-2352.
9 MAO Z， IIZUKA T， MAEDA S ．Bidirectional electrohydrodynamic pump with high symmetrical performance and its application to a tube actuator ［J］．Sensors and Actuators A：Physical，2021，332：113168-1-7.
10 孙博林，程敏，丁孺琦 ．带流量前馈与工作腔压力反馈的电液负载敏感系统节能方法 ［J］．液压与气动，2022，45（10）：1-7.
10 SUN Bolin， CHENG Min， DING Ruqi ．Energy sa-ving method for electro-hydraulic load-sensing systems with ［J］．Chinese Hydraulics & Pneumatics，2022，45（10）：1-7.
11 KOIVUM?KI J， MATTILA J ．Stability-guaranteed force-sensorless contact force/motion control of heavy-duty hydraulic manipulators ［J］．IEEE Transactions on Robotics，2015，31（4）：918-935.
12 KAMEZAKI M， IWATA H， SUGANO S ．Condition-based less-error data selection for robust and accurate mass measurement in large-scale hydraulic manipulators ［J］．IEEE Transactions on Instrumentation and Measurement，2017，66（7）：1820-1830.
13 RENNER A， WIND H， SAWODNY O ．Online payload estimation for hydraulically actuated manipulators ［J］．Mechatronics，2020，66：102322-1-15.
14 丁亚东，陈柏，吴洪涛，等 ．一种工业机器人动力学参数的辨识方法 ［J］．华南理工大学学报：（自然科学版），2015，43（3）：49-56.
14 DING Ya-dong， CHEN Bai， WU Hong-tao，et al ．An identification method of industrial robot's dynamic parameters［J］．Journal of South China University of Technology（Natural Science Edition），2015，43（3）：49-56.
15 VUONG N， MARCELO JR A H， LI Y，et al ．Improved dynamic identification of robotic manipulators in the linear region of dynamic friction ［J］．IFAC Proceedings Volumes，2009，42（16）：167-172.
16 郭菲，李永泉，李玉昆，等 ．电液驱动 3-UPS/S 并联稳定平台的动力学参数辨识 ［J］．中国机械工程，2016，27（21）：2862-2868，2876.
16 GUO Fei， LI Yongquan， LI Yukun，et al ．Indentification of dynamics parameters for an electro-hydraulic 3-UPS/S paralled stabilized paltform ［J］．Journal of Mechanical Engineering，2016，27（21）：2862-2868，2876.
17 张铁，胡亮亮，邹焱飚 ．基于混合遗传算法的机器人改进摩擦模型辨识 ［J］．浙江大学学报 （工学版），2021，55（5）：801-809.
17 ZHANG Tie， HU Liang-liang， ZOU Yan-biao ．Identification of improved friction model for robot based on hybrid genetic algorithm ［J］．Journal of Zhejiang University （Engineering Science），2021，55（5）：801-809.
18 李琳，林燕龙，邹焱飚 ．Scara 机器人关节摩擦建模与补偿的实验与仿真 ［J］．系统仿真学报，2019，31（8）：1572-1581.
18 LI Lin， LIN Yanlong， ZOU Yanbiao ．Simulation and experiment of friction modeling and compensation of scara ［J］．Journal of System Simulation，2019，31（8）：1572-1581.
19 吴文祥，朱世强，靳兴来 ．基于改进傅里叶级数的机器人动力学参数辨识 ［J］．浙江大学学报（工学版），2013，47（2）：231-237.
19 WU Wen-xiang， ZHU Shi-qiang， JIN Xing-lai ．Dynamic identification for robot manipulators based on modifiedFourierseries ［J］．Journal of Zhejiang University （Engineering Science），2013，47（2）：231-237.
20 席万强，陈柏，丁力，等 ．考虑非线性摩擦模型的机器人动力学参数辨识 ［J］．农业机械学报，2017，48（2）：393-399.
20 XI Wanqiang， CHEN Bai， DING Li，et al ．Dynamic parameter identification for robot manipulators with nonlinear friction model ［J］．Transactions of the Chinese Society for Agricultural Machinery，2017，48（2）：393-399.
21 贺苗，吴晓敏，邵桂芳，等 ．基于RBFNN的机器人关节摩擦建模与补偿研究 ［J］．仪器仪表学报，2020，41（11）：278-284.
21 HE Miao， WU Xiaomin， SHAO Guifang，et al ．Research on friction modeling and compensation of robot joint based on RBFNN ［J］．Chinese Journal of Scientific Instrument，2020，41（11）：278-284.
22 张铁，洪景东，李秋奋，等 ．基于 BP 神经网络的机器人波动摩擦力矩修正方法 ［J］．工程科学学报，2019，41（8）：1085-1091.
22 ZHANG Tie， HONG Jingdong， LI Qiufen，et al ．Wave friction correction method for a robot based on BP neural network ［J］．Chinese Journal of Engineering，2019，41（8）：1085-1091.
23 YANG H， FANG H， FANG Y，et al ．Dimensional synthesis of a novel 5-DOF reconfigurable hybrid perfusion manipulator for large-scale spherical honeycomb perfusion ［J］．Frontiers of Mechanical Engineering，2021，16（1）：46-60.
24 AI BITAR N， GAVRILOV A ．A new method for compensating the errors of integrated navigation systems using artificial neural networks ［J］．Measurement，2021，168：108391-1-12.
25 WANG S， SHAO X， YANG L，et al ．Deep learning aided dynamic parameter identification of 6-DOF robot manipulators ［J］．IEEE Access，2020，8：138102-138116.
26 XIAO J， ZENG F， ZHANG Q，et al ．Research on the forcefree control of cooperative robots based on dynamic parameters identification ［J］．Industrial Robot：the International Journal of Robotics Research and Application，2019，46（4）：499-509.
27 程文明，张道裕，谌庆荣，等 ．欠驱动起重机的神经网络直接鲁棒自适应控制 ［J］．华南理工大学学报（自然科学版），2021，49（5）：120-127，144.
27 CHENG Wenming， ZHANG Daoyu， CHEN Qingrong，et al ．A directly robust adaptive neural network controller for an underacuated crane［J］．Journal of South China University of Technology （Natural Science Edition），2021，49（5）：120-127，144.
28 LIU S， WANG L， WANG X V ．Sensorless force estimation for industrial robots using disturbance observer and neural learning of friction approximation ［J］．Robotics and Computer-Integrated Manufacturing，2021，71：102168-1-11.
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