Learning-Based Variable Admittance Control Combined with NMPC for Contact Force Tracking in Unknown Environments
With the development of robotics, robots are playing an increasingly critical role in complex tasks such as flexible manufacturing, physical human–robot interaction, and intelligent assembly. These tasks place higher demands on the force control performance of robots, particularly in scenarios where...
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MDPI AG
2025-06-01
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| Series: | Actuators |
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| Online Access: | https://www.mdpi.com/2076-0825/14/7/323 |
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| author | Yikun Zhang Jianjun Yao Chen Qian |
| author_facet | Yikun Zhang Jianjun Yao Chen Qian |
| author_sort | Yikun Zhang |
| collection | DOAJ |
| description | With the development of robotics, robots are playing an increasingly critical role in complex tasks such as flexible manufacturing, physical human–robot interaction, and intelligent assembly. These tasks place higher demands on the force control performance of robots, particularly in scenarios where the environment is unknown, making constant force control challenging. This study first analyzes the robot and its interaction model with the environment, highlighting the limitations of traditional force control methods in addressing unknown environmental stiffness. Based on this analysis, a variable admittance control strategy is proposed using the deep deterministic policy gradient algorithm, enabling the online tuning of admittance parameters through reinforcement learning. Furthermore, this strategy is integrated with a quaternion-based nonlinear model predictive control scheme, ensuring coordination between pose tracking and constant-force control and enhancing overall control performances. The experimental results demonstrate that the proposed method improves constant force control accuracy and task execution stability, validating the feasibility of the proposed approach. |
| format | Article |
| id | doaj-art-3642caaab478494b80e39aa24f0d4513 |
| institution | DOAJ |
| issn | 2076-0825 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj-art-3642caaab478494b80e39aa24f0d45132025-08-20T03:13:39ZengMDPI AGActuators2076-08252025-06-0114732310.3390/act14070323Learning-Based Variable Admittance Control Combined with NMPC for Contact Force Tracking in Unknown EnvironmentsYikun Zhang0Jianjun Yao1Chen Qian2College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, ChinaWith the development of robotics, robots are playing an increasingly critical role in complex tasks such as flexible manufacturing, physical human–robot interaction, and intelligent assembly. These tasks place higher demands on the force control performance of robots, particularly in scenarios where the environment is unknown, making constant force control challenging. This study first analyzes the robot and its interaction model with the environment, highlighting the limitations of traditional force control methods in addressing unknown environmental stiffness. Based on this analysis, a variable admittance control strategy is proposed using the deep deterministic policy gradient algorithm, enabling the online tuning of admittance parameters through reinforcement learning. Furthermore, this strategy is integrated with a quaternion-based nonlinear model predictive control scheme, ensuring coordination between pose tracking and constant-force control and enhancing overall control performances. The experimental results demonstrate that the proposed method improves constant force control accuracy and task execution stability, validating the feasibility of the proposed approach.https://www.mdpi.com/2076-0825/14/7/323contact force trackingvariable admittance controldeterministic policy gradient algorithmnonlinear model predictive control |
| spellingShingle | Yikun Zhang Jianjun Yao Chen Qian Learning-Based Variable Admittance Control Combined with NMPC for Contact Force Tracking in Unknown Environments Actuators contact force tracking variable admittance control deterministic policy gradient algorithm nonlinear model predictive control |
| title | Learning-Based Variable Admittance Control Combined with NMPC for Contact Force Tracking in Unknown Environments |
| title_full | Learning-Based Variable Admittance Control Combined with NMPC for Contact Force Tracking in Unknown Environments |
| title_fullStr | Learning-Based Variable Admittance Control Combined with NMPC for Contact Force Tracking in Unknown Environments |
| title_full_unstemmed | Learning-Based Variable Admittance Control Combined with NMPC for Contact Force Tracking in Unknown Environments |
| title_short | Learning-Based Variable Admittance Control Combined with NMPC for Contact Force Tracking in Unknown Environments |
| title_sort | learning based variable admittance control combined with nmpc for contact force tracking in unknown environments |
| topic | contact force tracking variable admittance control deterministic policy gradient algorithm nonlinear model predictive control |
| url | https://www.mdpi.com/2076-0825/14/7/323 |
| work_keys_str_mv | AT yikunzhang learningbasedvariableadmittancecontrolcombinedwithnmpcforcontactforcetrackinginunknownenvironments AT jianjunyao learningbasedvariableadmittancecontrolcombinedwithnmpcforcontactforcetrackinginunknownenvironments AT chenqian learningbasedvariableadmittancecontrolcombinedwithnmpcforcontactforcetrackinginunknownenvironments |