Impedance Identification and Adaptive Control of Rehabilitation Robot for Upper-Limb Passive Training

  • Aiguo Song
  • Lizheng Pan
  • Guozheng Xu
  • Huijun Li
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 213)


Rehabilitation robot can assist post-stroke patients during rehabilitation therapy. The movement control of the robot plays an important role in the process of functional recovery training. Owing to the change of the arm impedance of the post-stroke patient in the passive recovery training, the conventional movement control based on PI controller is difficult to produce smooth movement to track the designed trajectory set by the rehabilitation therapist. In this paper, we model the dynamics of post-stroke patient arm as an impedance model, and an adaptive control scheme which consists of an adaptive PI control algorithm and a damp control algorithm is proposed to control the rehabilitation robot moving along predefined trajectories stably and smoothly. An equivalent 2-port circuit of the rehabilitation robot and human arm is built, and passivity theory of circuit is used to analyze the stability and smoothness performance of the robot. A slide least mean square with adaptive window (SLMS-AW) method is presented to online estimate the parameters of the arm impedance model, which is used for adjusting the gains of PI-damp controller. In this paper, the Barrett WAM Arm manipulator is used as the main hardware platform for the functional recovery training of the post-stroke patient. Passive recovery training has been implemented on the WAM Arm. Experimental results demonstrate the effectiveness and potential of the proposed adaptive control strategies.


Rehabilitation robot Stroke Impedance model Parameter identification Robot control 



This work was supported by the National Natural Science Foundation of China (No. 61272379, 61104206), the Natural Science Foundation of JiangSu Province (BK2010063), and Foundation of ChangZhou (CE20120085).


  1. 1.
    Homepage of Chinese Ministry of Health.
  2. 2.
    Michel VEG, Driessen BJF, Michel D et al (2005) A Motorized gravity compensation mechanism used for Active Rehabilitation of upper limbs. In: Proceedings of the 2005 IEEE 9th international conference on rehabilitation robotics, Chicago, pp 152–155Google Scholar
  3. 3.
    Burgar CG, Lum PS, Shor PC et al (2000) Development of robots for rehabilitation therapy: the Polo Altova/Stanford experience. J Rehabil Res Dev 37(6):663–673Google Scholar
  4. 4.
    Reinkensmeyer DJ, Kahn LE, Arerbuch M et al (2000) Understanding and treating arm movement impairment after chronic brain injury: Progress with the ARM Guide. J Rehabil Res Dev 37(6):653–662Google Scholar
  5. 5.
    Krebs HI, Volpe BT, Aisen ML, Hogan N (2000) Increasing productivity and quality of care: robot-aided neuro-rehabilitation. J Rehabil Res Dev 37(6):639–652Google Scholar
  6. 6.
    Zhang YB, Wang ZX, Ji LH (2005) The clinical application of the upper extremity compound movements rehabilitation training robot. In: Proceedings of the 2005 IEEE 9th international conference on rehabilitation robotics, Chicago, pp 91–94Google Scholar
  7. 7.
    Krebs HI, Hogan N, Aisen ML, Volpe BT (1998) Robot-aided neurorehabilitation. IEEE Trans On Rehab Eng 6:75–87CrossRefGoogle Scholar
  8. 8.
    Kahn LE, Rymer WZ, Reinkensmeyer DJ (2004) Adaptive assistance for guided force training in chronic stroke. In: Proceedings of the 26th annual international conference of the IEEE EMBS, San Francisco, pp 2722–2725Google Scholar
  9. 9.
    Lindberg P, Schmitz C, Forssberg H (2004) Engardt: Effects of passive-active movement training on upper limb motor function and cortical activation in chronic patients with stroke: a pilot study. J Rehabil Med 36:117–123CrossRefGoogle Scholar
  10. 10.
    O’Malley MK, Sledd A, Gupta A (2006) The rice wrist: a distal upper extremity rehabilitation robot for stroke therapy. In: Proceedings IMECE, Chicago, pp 1–10Google Scholar
  11. 11.
    Duygun E, Vishnu M, Nilanjan S et al (2005) A new control approach to robot assisted rehabilitation. In: Proceedings of the 2005 IEEE 9th international conference on rehabilitation robotics, Chicago, pp 323–328Google Scholar
  12. 12.
    Xu GZ, Song AG, Li HJ (2011) Control system design for an upper-limb rehabilitation robot. Adv Rob 25(1):229–251CrossRefGoogle Scholar
  13. 13.
    Xu GZ, Song AG, Li HJ (2011) Adaptive impedance control for upper-limb rehabilitation robot using evolutionary dynamic recurrent fuzzy neural networks. J Intell Rob Syst 62(2):501–525CrossRefMATHGoogle Scholar
  14. 14.
    Anderson RJ, Spong MW (1989) Bilateral control of teleoperators with time delay. IEEE Trans Autom Control 34(5):494–501MathSciNetCrossRefGoogle Scholar
  15. 15.
    Li HJ, Song AG (2007) Virtual-environment modeling and correction for force-reflecting teleoperation with time delay. IEEE Trans Ind Elec 54(2):1227–1233CrossRefGoogle Scholar
  16. 16.
    Lin CC, Ju MS, Lin CW et al (2003) The pendulum test for evaluating spasticity of the elbow joint. Arch Phys Med Rehabil 84:69–74CrossRefGoogle Scholar
  17. 17.
    Noritsugu T, Tanaka T (1997) Application of rubber artificial muscle manipulator as a rehabilitation robot. IEEE/ASME Trans Mechatronics 2(4):259–267CrossRefGoogle Scholar
  18. 18.
    Song AG, Wu J, Qin G, Huang WY (2007) A novel self-decoupled four degree-of-freedom wrist force/torque sensor. Measurement 40(9): 883–889Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Aiguo Song
    • 1
  • Lizheng Pan
    • 1
  • Guozheng Xu
    • 1
  • Huijun Li
    • 1
  1. 1.School of Instrument Science and EngineeringSoutheast UniversityNanjingChina

Personalised recommendations