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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Bibliographic Details
Published in:Journal of intelligent & robotic systems 2023-11, Vol.109 (3), p.48, Article 48
Main Authors: Dong, Bo, Jing, Yusheng, Zhu, Xinye, Cui, Yiming, An, Tianjiao
Format: Article
Language:English
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
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Summary: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.
ISSN:0921-0296
1573-0409
DOI:10.1007/s10846-023-01978-0