Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 64, Issue 2, Pages 1664-1674Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2016.2538741
Keywords
Adaptive impedance control; high-gain observer; neural networks; robotic exoskeleton
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Funding
- National Natural Science Foundation of China [61573147, 91520201]
- Guangzhou Research Collaborative Innovation Projects [2014Y2-00507]
- Guangdong Science and Technology Research Collaborative Innovation Projects [2014B090901056]
- Guangdong Science and Technology Plan Project (Application Technology Research Foundation) [2015B020233006]
- National High-Tech Research and Development Program of China (863 Program) [2015AA042303]
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This paper presents adaptive impedance control of an upper limb robotic exoskeleton using biological signals. First, we develop a reference musculoskeletal model of the human upper limb and experimentally calibrate the model to match the operator's motion behavior. Then, the proposed novel impedance algorithm transfers stiffness from human operator through the surface electromyography (sEMG) signals, being utilized to design the optimal reference impedance model. Considering the unknown deadzone effects in the robot joints and the absence of the precise knowledge of the robot's dynamics, an adaptive neural network control incorporating with a high-gain observer is developed to approximate the deadzone effect and robot's dynamics and drive the robot tracking desired trajectories without velocity measurements. In order to verify the robustness of the proposed approach, the actual implementation has been performed using a real robotic exoskeleton and a human operator.
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