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Dynamics Based Fuzzy Adaptive Impedance Control for Lower Limb Rehabilitation Robot

  • Xu Liang
  • Weiqun Wang
  • Zengguang Hou
  • Zihao Xu
  • Shixin Ren
  • Jiaxing Wang
  • Liang Peng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11307)

Abstract

Human-robot interaction control plays a significant role in the research and clinical application of rehabilitation robots. A fuzzy adaptive variable impedance control strategy is proposed in this paper. Firstly, a dynamic model is established by using the Lagrangian method and the traditional friction model, which can be used to predict human-robot interaction forces. Then, a fuzzy adaptive variable impedance control strategy based on the human-robot system dynamic model is designed. In the designed control strategy, the interaction forces, position and velocity errors are taken as the system inputs, and a fuzzy adaptive law is used to adjust the damping and stiffness coefficients. Finally, the dynamics identification experiments and simulation of the fuzzy adaptive variable impedance control strategy are carried out, by which performance of the proposed method is validated.

Keywords

Fuzzy adaptive impedance Dynamics Parameter identification Assist-as-needed Rehabilitation robot 

References

  1. 1.
    Von Schroeder, H.P., Coutts, R.D., Lyden, P.D., Billings Jr., E., Nickel, V.L.: Gait parameters following stroke: a practical assessment. J. Rehabil. Res. Develop. 32, 25–31 (1995)Google Scholar
  2. 2.
    Olney, S.J., Griffin, M.P., Monga, T.N., McBride, I.D.: Work and power in gait of stroke patients. Arch. Phys. Med. Rehabil. 72, 309–314 (1991)Google Scholar
  3. 3.
    Banala, S.K., Kim, S.H., Agrawal, S.K., Scholz, J.P.: Robot assisted gait training with active leg exoskeleton (ALEX). IEEE Trans. Neural Syst. Rehabil. Eng. 17, 2–8 (2009)CrossRefGoogle Scholar
  4. 4.
    Cai, L.L., et al.: Implications of assist-as-needed robotic step training after a complete spinal cord injury on intrinsic strategies of motor learning. J. NeuroSci. 26, 10564–10568 (2006)CrossRefGoogle Scholar
  5. 5.
    Riener, R., Lunenburger, L., Jezernik, S., Anderschitz, M., Colombo, G., Dietz, V.: Patient-cooperative strategies for robot-aided treadmill training: First experimental results. IEEE Trans. Neural Syst. Rehabil. Eng. 13, 380–394 (2005)CrossRefGoogle Scholar
  6. 6.
    Pons, T.P., Garraghty, P.E., Ommaya, A.K., Kaas, J.H., Taub, E., Mishkin, M.: Massive cortical reorganization after sensory deafferentation in adult macaques. Science 252, 1857–1860 (1991)CrossRefGoogle Scholar
  7. 7.
    Lee, H., Hogan, N.: Time-varying ankle mechanical impedance during human locomotion. IEEE Trans. Neural Syst. Rehabil. Eng. 23, 755–764 (2015)CrossRefGoogle Scholar
  8. 8.
    Mendoza, M., Bonilla, I., González-Galván, E., Reyes, F.: Impedance control in a wave-based teleoperator for rehabilitation motor therapies assisted by robots. Comput. Meth. Prog. Bio. 123, 54–67 (2016)CrossRefGoogle Scholar
  9. 9.
    Ficuciello, F., Villani, L., Siciliano, B.: Variable impedance control of redundant manipulators for intuitive human-robot physical interaction. IEEE Trans. Robot. 31, 850–863 (2015)CrossRefGoogle Scholar
  10. 10.
    Liu, M., Zhang, F., Datseris, P., Huang, H.: Improving finite state impedance control of active-transfemoral prosthesis using dempster-shafer based state transition rules. J. Intell. Robot. Syst. 76, 461–474 (2014)CrossRefGoogle Scholar
  11. 11.
    Huang, H., Crouch, D.L., Liu, M., Sawicki, G.S., Wang, D.: A cyber expert system for auto-tuning powered prosthesis impedance control parameters. Ann. Biomed. Eng. 44, 1613–1624 (2016)CrossRefGoogle Scholar
  12. 12.
    Wit, C.C.D., Noel, P., Aubin, A., Brogliato, B., Drevet, P.: Adaptive friction compensation in robot manipulators: low velocities. Int. J. Robot. Res. 10, 1352–1357 (1991)Google Scholar
  13. 13.
    Wei, L.Y., Qi, H., Ren, Y.T., Sun, J.P., Wen, S., Ruan, L.M.: Application of hybrid SPSO-SQP algorithm for simultaneous estimation of space-dependent absorption coefficient and scattering coefficient fields in participating media. Int. J. Therm. Sci. 124, 424–432 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xu Liang
    • 1
    • 2
  • Weiqun Wang
    • 1
  • Zengguang Hou
    • 1
    • 2
  • Zihao Xu
    • 3
  • Shixin Ren
    • 1
    • 2
  • Jiaxing Wang
    • 1
    • 2
  • Liang Peng
    • 1
  1. 1.Institute of Automation, Chinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Lanzhou Jiaotong UniversityLanzhouChina

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