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Integrated Observer-based Fixed-time Control with Backstepping Method for Exoskeleton Robot

  • Gao-Wei Zhang
  • Peng Yang
  • Jie WangEmail author
  • Jian-Jun Sun
  • Yan Zhang
Research Article Special Issue on Improving Productivity through Automation and Computing
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Abstract

To achieve the fast convergence and tracking precision of a robotic upper-limb exoskeleton, this paper proposes an observer-based integrated fixed-time control scheme with a backstepping method. Firstly, a typical 5 DoF (degrees of freedom) dynamics is constructed by Lagrange equations and processed for control purposes. Secondly, second-order sliding mode controllers (SOSMC) are developed and novel sliding mode surfaces are introduced to ensure the fixed-time convergence of the human-robot system. Both the reaching time and settling time are proved to be bounded with certain values independent of initial system conditions. For the purpose of rejecting the matched and unmatched disturbances, nonlinear fixed-time observers are employed to estimate the exact value of disturbances and compensate the controllers online. Ultimately, the synthesis of controllers and disturbance observers is adopted to achieve the excellent tracking performance and simulations are given to verify the effectiveness of the proposed control strategy.

Keywords

Upper-limb exoskeleton sliding mode control (SMC) fixed-time control disturbance observe backstepping 

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Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (Nos. 61703134, 61703135, 61773151, 61503118 and 61871173), Natural Science Foundation of Hebei Province (Nos. F2015202150, F2016202327 and F2018202279), Natural Science Foundation of Tianjin (No. 17JCQNJC04400), the Foundation of Hebei Educational Committee (Nos. QN2015068 and ZD2016071), the Colleges and Universities in Hebei Province Science and Technology Research Youth Fund (No. ZC2016020) and the Graduate Innovation Funding Project of Hebei Province (No. CXZZBS2017038).

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Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Artificial IntelligenceHebei University of TechnologyTianjinChina
  2. 2.Enginnering Research Center of Intelligent Rehabilitation and Detection TechnologyMinistry of EducationTianjinChina

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