Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Robotics and Applications

ICIRA 2022: Intelligent Robotics and Applications pp 138–149Cite as

Impedance Control of Upper Limb Rehabilitation Robot Based on Series Elastic Actuator

  • Conference paper
  • First Online:

  • 2682 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13457))

Abstract

In this paper, to address motor dysfunction caused by factors such as stroke or traffic accidents, a kind of upper limb rehabilitation robot is designed for rehabilitation training. The rehabilitation robot is driven by series elastic actuator (SEA) to make the upper limb rehabilitation robot have flexible output. Flexible output can improve the compliance and safety between the patient and the rehabilitation robot, but impedance control method is needed to ensure the compliance of human–robot interaction. In order to solve the human–robot interaction problem of SEA–based upper limb rehabilitation robot, the dynamic model and an impedance control are established for the SEA–based upper limb rehabilitation robot. The impedance control method of upper limb rehabilitation robot based on terminal position is designed in detail. Aiming at the designed impedance control method, a numerical simulation model is established for the upper limb rehabilitation robot, and the accuracy of the model is verified by the simulation of the upper limb rehabilitation robot. The numerical results show that the impedance controller can meet the needs of the rehabilitation training of the upper limb rehabilitation robot, which improves the coordination of human–robot interaction in the rehabilitation process.

The work is supported in part by the National Natural Science Foundation of China under grants 62173048, 61873304 and in part by the China Postdoctoral Science Foundation Funded Project under grants 2018M641784 and 2019T120240, and also in part by the Changchun Science and Technology Project under grant 21ZY41.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bertani, R., Melegari, C., De Cola, M.C., Bramanti, A., Bramanti, P., Calabrò, R.S.: Effects of robot-assisted upper limb rehabilitation in stroke patients: a systematic review with meta-analysis. Neurol. Sci. 38(9), 1561–1569 (2017). https://doi.org/10.1007/s10072-017-2995-5

    Article  Google Scholar 

  2. Sun, Z.B., Liu, Y., Wang, G., et al.: Discrete-time noise-tolerant Z-type model for online solving nonlinear time-varying equations in the presence of noises. J. Comput. Appl. Math. 403(15), 113824 (2022)

    Article  MathSciNet  Google Scholar 

  3. Akdo, A.E., Aktan, M.E., Koru, A.T., et al.: Hybrid impedance control of a robot manipulator for wrist and forearm rehabilitation: performance analysis and clinical results. Mechatronics 49, 77–91 (2017)

    Google Scholar 

  4. Brahmi, B., Driscoll, M., El Bojairami, I.K., Saad, M., Brahmi, A.: Novel adaptive impedance control for exoskeleton robot for rehabilitation using a nonlinear time-delay disturbance observer. ISA Trans. 108, 381–392 (2020)

    Article  Google Scholar 

  5. Wu, Q., Wang, X., Bai, C., et al.: Development of an RBFN-based neural-fuzzy adaptive control strategy for an upper limb rehabilitation exoskeleton. Mechatronics 53, 85–94 (2018)

    Article  Google Scholar 

  6. Jalaeian, F.M., Fateh, M.M., Rahimiyan, M.: Optimal predictive impedance control in the presence of uncertainty for a lower limb rehabilitation robot. J. Syst. Sci. Complexity 33, 310–1329 (2020)

    Article  MathSciNet  Google Scholar 

  7. Mancisidor, A., Zubizarretaa, A., Cabanes, I., et al.: Kinematical and dynamical modeling of a multipurpose upper limbs rehabilitation robot. Robot. Comput. Integr. Manuf. 49(7), 374–387 (2018)

    Article  Google Scholar 

  8. Yang, T., Gao, X., Dai, F.: New hybrid AD methodology for minimizing the total amount of information content: a case study of rehabilitation robot design. Chin. J. Mech. Eng. 33(1), 51–60 (2020)

    Article  Google Scholar 

  9. Sun, Z., Zhao, L., Liu, K., Jin, L., Yu, J., Li, C.: An advanced form-finding of tensegrity structures aided with noise-tolerant zeroing neural network. Neural Comput. Appl. 34(8), 6053–6066 (2021). https://doi.org/10.1007/s00521-021-06745-6

    Article  Google Scholar 

  10. Sun, Z.B., Wang, G., Jin, L., et al.: Noise-suppressing zeroing neural network for online solving time-varying matrix square roots problems: a control-theoretic approach. Expert Syst. Appl. 192(15), 116272 (2022)

    Article  Google Scholar 

  11. Ji, L.: Quantitative assessment of motor function by an end-effector upper limb rehabilitation robot based on admittance control. Appl. Sci. 11, 112–132 (2021)

    Google Scholar 

  12. Meng, Q., Jiao, Z., Yu, H., et al.: Design and evaluation of a novel upper limb rehabilitation robot with space training based on an end effector. Mech. Sci. 1, 639–648 (2021)

    Article  Google Scholar 

  13. Madani, M., Moallem, M.: Hybrid position/force control of a flexible parallel manipulator. J. Franklin Inst. 348(6), 999–1012 (2011)

    Article  Google Scholar 

  14. Wang, J., Liu, J., Zhang, G., et al.: Periodic event-triggered sliding mode control for lower limb exoskeleton based on human-robot cooperation. ISA Trans. 123, 87–97 (2022)

    Article  Google Scholar 

  15. Chai, Y.Y., Liu, K.P., Li, C.X., et al.: A novel method based on long short term memory network and discrete-time zeroing neural algorithm for upper-limb continuous estimation using sEMG signals. Biomed. Signal Process. Control 67, 1746–8094 (2021)

    Article  Google Scholar 

  16. Krebs, H.I., Volpe, B.T., Aisen, M.L., et al.: Increasing productivity and quality of care: robot-aided neuro-rehabilitation. J. Rehabil. Res. Dev. 37(6), 639–652 (2000)

    Google Scholar 

  17. Zhang, Q., Sun, D., Qian, W., et al.: Modeling and control of a cable-driven rotary series elastic actuator for an upper limb rehabilitation robot. Front. Neurorobot. 14, 13 (2020)

    Article  Google Scholar 

  18. Chen, T., Casas, R., Lum, P.S.: An elbow exoskeleton for upper limb rehabilitation with series elastic actuator and cable-driven differential. IEEE Trans. Robot. 35(6), 1464–1474 (2019)

    Article  Google Scholar 

  19. Banala, S.K., Kim, S.H., Agrawal, S.K., et al.: Robot assisted gait training with active leg exoskeleton (ALEX). IEEE Trans. Neural Syst. Rehabil. Eng. 17(1), 2–8 (2009)

    Article  Google Scholar 

  20. Hu, J., Hou, Z., Zhang, F., et al.: Training strategies for a lower limb rehabilitation robot based on impedance control. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6032–6035 (2012)

    Google Scholar 

  21. Riener, R., Lunenburger, L., Jezernik, S., et al.: Patient-cooperative strategies for robot-aided treadmill training: first experimental results. IEEE Trans. Neural Syst. Rehabil. Eng. 13(3), 380–394 (2055)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Control Engineering, Changchun University of Technology, Changchun, 130012, People’s Republic of China

    Jian Gu, Changxian Xu, Keping Liu, Liming Zhao, Tianyu He & Zhongbo Sun

  2. School of Electrical and Information Engineering, Jilin Engineering Normal University, Changchun, 130052, China

    Keping Liu

Authors
  1. Jian Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Changxian Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Keping Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Liming Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tianyu He
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhongbo Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Keping Liu .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Honghai Liu

  2. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  3. Shenyang Institute of Automation, Shenyang, Liaoning, China

    Lianqing Liu

  4. Harbin Institute of Technology, Harbin, China

    Li Jiang

  5. Shanghai Jiao Tong University, Shanghai, China

    Guoying Gu

  6. Shenzhen Institutes of Advanced Technology, Shenzhen, China

    Xinyu Wu

  7. Harbin Institute of Technology, Harbin, China

    Weihong Ren

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gu, J., Xu, C., Liu, K., Zhao, L., He, T., Sun, Z. (2022). Impedance Control of Upper Limb Rehabilitation Robot Based on Series Elastic Actuator. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13457. Springer, Cham. https://doi.org/10.1007/978-3-031-13835-5_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-13835-5_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13834-8

  • Online ISBN: 978-3-031-13835-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation