Observer-Based Adaptive Robust Force Control of a Robotic Manipulator Integrated with External Force/Torque Sensor
Maintaining precise interaction force in uncertain environments characterized by unknown and varying stiffness or location is significantly challenging for robotic manipulators. Existing approaches widely employ a two-level control structure in which the higher level generates the command motion of...
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MDPI AG
2025-02-01
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| author | Zixuan Huo Mingxing Yuan Shuaikang Zhang Xuebo Zhang |
| author_facet | Zixuan Huo Mingxing Yuan Shuaikang Zhang Xuebo Zhang |
| author_sort | Zixuan Huo |
| collection | DOAJ |
| description | Maintaining precise interaction force in uncertain environments characterized by unknown and varying stiffness or location is significantly challenging for robotic manipulators. Existing approaches widely employ a two-level control structure in which the higher level generates the command motion of the lower level according to the force tracking error. However, the low-level motion tracking error is generally ignored completely. Recognizing this limitation, this paper first formulates the low-level motion tracking error as an unknown input disturbance, based on which a dynamic interaction model capturing both structured and unstructured uncertainties is developed. With the developed interaction model, an observer-based adaptive robust force controller is proposed to achieve accurate and robust force modulation for a robotic manipulator. Alongside the theoretical stability analysis, comparative experiments with the classical admittance control (AC), the adaptive variable impedance control (AVIC), and the adaptive force tracking admittance control based on disturbance observer (AFTAC) are conducted on a robotic manipulator across four scenarios. The experimental results demonstrate the significant advantages of the proposed approach over existing methods in terms of accuracy and robustness in interaction force control. For instance, the proposed method reduces the root mean square error (RMSE) by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>91.3</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>87.2</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>75.5</mn><mo>%</mo></mrow></semantics></math></inline-formula> in comparison to AC, AVIC, and AFTAC, respectively, in the experimental scenario where the manipulator is directed to follow a time-varying force while experiencing significant low-level motion tracking errors. |
| format | Article |
| id | doaj-art-dcda71a59b4a49fd99c11c8f2aee87e2 |
| institution | Kabale University |
| issn | 2076-0825 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj-art-dcda71a59b4a49fd99c11c8f2aee87e22025-08-20T03:40:41ZengMDPI AGActuators2076-08252025-02-0114311610.3390/act14030116Observer-Based Adaptive Robust Force Control of a Robotic Manipulator Integrated with External Force/Torque SensorZixuan Huo0Mingxing Yuan1Shuaikang Zhang2Xuebo Zhang3College of Artificial Intelligence, Nankai University, Tianjin 300350, ChinaCollege of Artificial Intelligence, Nankai University, Tianjin 300350, ChinaCollege of Artificial Intelligence, Nankai University, Tianjin 300350, ChinaCollege of Artificial Intelligence, Nankai University, Tianjin 300350, ChinaMaintaining precise interaction force in uncertain environments characterized by unknown and varying stiffness or location is significantly challenging for robotic manipulators. Existing approaches widely employ a two-level control structure in which the higher level generates the command motion of the lower level according to the force tracking error. However, the low-level motion tracking error is generally ignored completely. Recognizing this limitation, this paper first formulates the low-level motion tracking error as an unknown input disturbance, based on which a dynamic interaction model capturing both structured and unstructured uncertainties is developed. With the developed interaction model, an observer-based adaptive robust force controller is proposed to achieve accurate and robust force modulation for a robotic manipulator. Alongside the theoretical stability analysis, comparative experiments with the classical admittance control (AC), the adaptive variable impedance control (AVIC), and the adaptive force tracking admittance control based on disturbance observer (AFTAC) are conducted on a robotic manipulator across four scenarios. The experimental results demonstrate the significant advantages of the proposed approach over existing methods in terms of accuracy and robustness in interaction force control. For instance, the proposed method reduces the root mean square error (RMSE) by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>91.3</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>87.2</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>75.5</mn><mo>%</mo></mrow></semantics></math></inline-formula> in comparison to AC, AVIC, and AFTAC, respectively, in the experimental scenario where the manipulator is directed to follow a time-varying force while experiencing significant low-level motion tracking errors.https://www.mdpi.com/2076-0825/14/3/116adaptive robust controlextended state observer (ESO)force controlroboticsuncertainties |
| spellingShingle | Zixuan Huo Mingxing Yuan Shuaikang Zhang Xuebo Zhang Observer-Based Adaptive Robust Force Control of a Robotic Manipulator Integrated with External Force/Torque Sensor Actuators adaptive robust control extended state observer (ESO) force control robotics uncertainties |
| title | Observer-Based Adaptive Robust Force Control of a Robotic Manipulator Integrated with External Force/Torque Sensor |
| title_full | Observer-Based Adaptive Robust Force Control of a Robotic Manipulator Integrated with External Force/Torque Sensor |
| title_fullStr | Observer-Based Adaptive Robust Force Control of a Robotic Manipulator Integrated with External Force/Torque Sensor |
| title_full_unstemmed | Observer-Based Adaptive Robust Force Control of a Robotic Manipulator Integrated with External Force/Torque Sensor |
| title_short | Observer-Based Adaptive Robust Force Control of a Robotic Manipulator Integrated with External Force/Torque Sensor |
| title_sort | observer based adaptive robust force control of a robotic manipulator integrated with external force torque sensor |
| topic | adaptive robust control extended state observer (ESO) force control robotics uncertainties |
| url | https://www.mdpi.com/2076-0825/14/3/116 |
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