Adaptive Impedance Decentralized Control of Modular Robot Manipulators for Physical Human-robot Interaction
For the problem of dynamic contact force tracking control under physical human-robot interaction (pHRI), we propose a dual closed-loop adaptive decentralized control framework. The dynamic model of modular robot manipulator (MRM) subsystem is established based on joint torque feedback (JTF) technolo...
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| Veröffentlicht in: | Journal of intelligent & robotic systems 2023-11, Vol.109 (3), p.48, Article 48 |
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| container_title | Journal of intelligent & robotic systems |
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| creator | Dong, Bo Jing, Yusheng Zhu, Xinye Cui, Yiming An, Tianjiao |
| description | For the problem of dynamic contact force tracking control under physical human-robot interaction (pHRI), we propose a dual closed-loop adaptive decentralized control framework. The dynamic model of modular robot manipulator (MRM) subsystem is established based on joint torque feedback (JTF) technology. On the basis of fully analyzing the model uncertainty, the method based on decomposition is used to dynamically compensate the model uncertainty. Using Lyapunov theory, the uniform and ultimate boundedness (UUB) of dynamic contact force tracking error and MRM position tracking error in pHRI process are confirmed. A neural network (NN) observer is designed to dynamically compensate the uncertainty of controller. Finally, the effectiveness of this method is verified by experiments. |
| doi_str_mv | 10.1007/s10846-023-01978-0 |
| format | Article |
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The dynamic model of modular robot manipulator (MRM) subsystem is established based on joint torque feedback (JTF) technology. On the basis of fully analyzing the model uncertainty, the method based on decomposition is used to dynamically compensate the model uncertainty. Using Lyapunov theory, the uniform and ultimate boundedness (UUB) of dynamic contact force tracking error and MRM position tracking error in pHRI process are confirmed. A neural network (NN) observer is designed to dynamically compensate the uncertainty of controller. 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The dynamic model of modular robot manipulator (MRM) subsystem is established based on joint torque feedback (JTF) technology. On the basis of fully analyzing the model uncertainty, the method based on decomposition is used to dynamically compensate the model uncertainty. Using Lyapunov theory, the uniform and ultimate boundedness (UUB) of dynamic contact force tracking error and MRM position tracking error in pHRI process are confirmed. A neural network (NN) observer is designed to dynamically compensate the uncertainty of controller. Finally, the effectiveness of this method is verified by experiments.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s10846-023-01978-0</doi></addata></record> |
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| subjects | Adaptive control Artificial Intelligence Closed loops Contact force Control Control algorithms Controllers Cooperation Decentralized control Decomposition Dynamic models Electrical Engineering Engineering Human engineering Manipulators Mechanical Engineering Mechatronics Neural networks Regular Paper Robot arms Robot control Robotics Robots Sensors Subsystems Teaching methods Tracking control Tracking errors Uncertainty |
| title | Adaptive Impedance Decentralized Control of Modular Robot Manipulators for Physical Human-robot Interaction |
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