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Cluster Computing

, Volume 20, Issue 4, pp 2855–2868 | Cite as

Iterative learning control applied to a hybrid rehabilitation exoskeleton system powered by PAM and FES

  • Xikai Tu
  • Xuan Zhou
  • Jiaxin Li
  • Chen SuEmail author
  • Xiaotong Sun
  • Hualin Han
  • Xiaobo Jiang
  • Jiping HeEmail author
Article

Abstract

Currently upper limb exoskeleton rehabilitation robots powered by electric motors used in the hospitals are usually cumbersome, bulky and unmovable. Our developed RUPERT is a low-cost lightweight portable exoskeleton rehabilitation robot that can encourage stroke patients with high stiffness in arm flexor muscles to receive frequent intensive rehabilitation trainings in the community or home, but its joints are unidirectionally actuated by pneumatic artificial muscles (PAMs). RUPERT with one PAM of each joint is not suitable for stroke patients with weak muscles in the flaccid paralysis period. Functional electrical stimulation (FES) uses current with low frequency to activate paralyzed muscles, which can produce muscle torque and compensate the unidirectional drawbacks of RUPERT, so as to realize the two-way motion of its joints for passive reaching trainings. As both the exoskeleton robot driven by PAMs and neuromuscular skeletal system under FES possess the highly nonlinear and time-varying characteristics, which adds control difficulty to the hybrid dynamic system, iterative learning control (ILC) is chosen to control this newly designed hybrid rehabilitation system to realize repetitive task trainings.

Keywords

ILC Exoskeleton Rehabilitation robot PMA FES Muscle model Upper limb rehabilitation 

Notes

Acknowledgements

This work was supported by “Research Initiation Funds for Ph.D” of Hubei University of Technology (Grant No. BSQD2016051), “Green Industry Technology Leading Program Project” of Hubei University of Technology (Grant No. ZZTS2017008), and in part by the National Natural Science Foundation of China under Grant No. 91648207.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Industrial DesignHubei University of TechnologyWuhanChina
  2. 2.Advanced Innovation Center for Intelligent Robots and SystemsBeijing Institute of TechnologyBeijingChina
  3. 3.Arizona State UniversityTempeUSA

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