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Adaptive motion control of arm rehabilitation robot based on impedance identification

Published online by Cambridge University Press:  01 May 2014

Aiguo Song ,
Lizheng Pan ,
Guozheng Xu  and
Huijun Li
Aiguo Song*
Affiliation:
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
Lizheng Pan
Affiliation:
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
Guozheng Xu
Affiliation:
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
Huijun Li
Affiliation:
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
*
*Corresponding author. E-mail: a.g.song@seu.edu.cn
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Summary

There is increasing interest in using rehabilitation robots to assist post-stroke patients during rehabilitation therapy. The motion control of the robot plays an important role in the process of functional recovery training. Due to the change of the arm impedance of the post-stroke patient in the passive recovery training, the conventional motion control based on a proportional-integral (PI) controller is difficult to produce smooth movement of the robot 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 propose an adaptive control scheme, which consists of an adaptive PI control algorithm and an adaptive damping control algorithm, to control the rehabilitation robot moving along predefined trajectories stably and smoothly. An equivalent two-port circuit of the rehabilitation robot and human arm is built, and the passivity theory of circuits 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 for on-line estimation of the parameters of the arm impedance model, which is used for adjusting the gains of the PI-damping 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, and the experimental results demonstrate the effectiveness and potential of the proposed adaptive control strategies.

Keywords

Rehabilitation robotStrokeImpedance modelParameter identificationRobot control

Information

Type
Articles
Information
Robotica , Volume 33 , Issue 9 , November 2015 , pp. 1795 - 1812
Copyright
Copyright © Cambridge University Press 2014 

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References

1. Homepage of Chinese Ministry of Health, Available at http://www.moh.gov.cn/public/.Google Scholar
2. Kwakkel, G., Kollen, B. J. and Krebs, H. I., “Effects of robot-assisted therapy on upper limb recovery after stroke: A systematic review,” Neurorehabil Neural Repair 22 (2), 111–121 (2008).Google Scholar
3. Laura, M. C. and David, J. R.. “Review of control strategies for robotic movement training after neurologic injury,” J. NeuroEng. Rehabil 6, 20 (2009).Google Scholar
4. Lum, P. S., Burgar, C. G., Machiel, V. D. L., Shor, P. C., Matra, M. and Ruth, Y.. “MIME robotic device for upper-limb neurorehabilitation in subacute stroke subjects: A follow-up study,” J. Rehabil. Res. Dev. 43 (5), 631–642 (2006).Google Scholar
5. Reinkensmeyer, D. J., Kahn, L. E., Arerbuch, M., Alicia, M. C., Schmit, B. D. and Rymer, W. Z.. “Understanding and treating arm movement impairment after chronic brain injury: Progress with the ARM Guide,” J. Rehabil. Res. Dev. 37 (6), 653–662 (2000).Google Scholar
6. Krebs, H. I., Volpe, B. T., Aisen, M. L., Hening, W., Adamovich, S., Poizner, H., Subrahmanyan, K. and Hogan, N.. “Robotic applications in neuromotor rehabilitation,” Robotica 22, 3–11 (2003).Google Scholar
7. Zhang, Y. B., Wang, Z. X., Ji, L. H. and Bi, S., “The Clinical Application of the Upper Extremity Compound Movements Rehabilitation Training Robot,” Proceedings of the 2005 IEEE 9th International Conference on Rehabilitation Robotics, Chicago, USA (Jun. 28–Jul. 1, 2005) pp. 91–94.Google Scholar
8. Krebs, H. I., Hogan, N., Aisen, M. L. and Volpe, B. T., “Robot-aided neurorehabilitation,” IEEE Trans. Rehabil. Eng. 6, 75–87 (1998).Google Scholar
9. Eva, S., Saartje, D., Baeyens, J. P., Meeusen, R. and Eric, K., “Effectiveness of robot-assisted gait training in persons with spinal cord injury: A systematic review,” J. Rehabil. Med. 42, 520–526 (2010).Google Scholar
10. Mihelj, M., “Human arm kinematics for robot based rehabilitation,” Robotica 24, 377–383 (2006).Google Scholar
11. O'Malley, M. K., Sledd, A., Gupta, A., Patoglu, V. and Huegel, J., “The Rice Wrist: A Distal Upper Extremity Rehabilitation Robot for Stroke Therapy,” Proceedings of the IMECE, Chicago, USA (2006) pp. 1–10.Google Scholar
12. Duygun, E., Vishnu, M., Nilanjan, S. and Edward, T.. “A New Control Approach to Robot Assisted Rehabilitation,” Proceedings of the 2005 IEEE 9th International Conference on Rehabilitation Robotics, Chicago IL, USA (Jun. 28–Jul. 1, 2005) pp. 323–328.Google Scholar
13. Xu, G. Z., Song, A. G. and Li, H. J., “Control system design for an upper-limb rehabilitation robot,” Adv. Robot 25 (1), 229–251 (2011).Google Scholar
14. Xu, G. Z., Song, A. G. and Li, H. J., “Adaptive impedance control for upper-limb rehabilitation robot using evolutionary dynamic recurrent fuzzy neural networks,” J. Intell. Robot Syst 62 (2), 501–525 (2011).Google Scholar
15. Anderson, R. J. and Spong, M. W., “Bilateral control of teleoperators with time delay,” IEEE Trans. Autom. Control 34 (5), 494–501 (1989).Google Scholar
16. Li, H. J. and Song, A. G., “Virtual-environment modeling and correction for force-reflecting teleoperation with time delay,” IEEE Trans. Ind. Electron. 54 (2), 1227–1233 (2007).Google Scholar
17. Lin, C. C., Ju, M. S. and Lin, C. W.. “The pendulum test for evaluating spasticity of the elbow joint,” Arch. Phys. Med. Rehabil. 84, 69–74 (2003).Google Scholar
18. Noritsugu, T. and Tanaka, T., “Application of rubber artificial muscle manipulator as a rehabilitation robot,” IEEE/ASME Trans. Mechatronics 2 (4), 259–267 (1997).Google Scholar
19. Song, A. G., Wu, J., Qin, G. and Huang, W. Y., “A novel self-decoupled four degree-of-freedom wrist force/torque sensor,” Measurement 40 (9), 883–889 (2007).Google Scholar