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Handling subject arm uncertainties for upper limb rehabilitation robot using robust sliding mode control

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Abstract

Upper Limb Rehabilitation Robots (ULRR) for the patient having shoulder and elbow joint movement disorders, requires further study for development. One aspect that must be fulfilled by such robots, is the need to handle uncertainties due to biomechanical variation of different patients, without significantly degrading performance. Currently, rehabilitation robots require re-tuning of controller gain for each individual. This is time consuming process and requires expert training. To overcome this problem, we propose robust sliding mode control algorithm, which uses very basic information of subject like weight, height, age and gender to handle these model uncertainties. For analysis, we have compared our proposed algorithm with Robust Computed Torque Control (RCTC) and Boundary Augmented Sliding Mode Control (BASMC) algorithms with diverse subjects. Results describe the superiority of the proposed algorithm in handling uncertain parameters human arm and robot without degrading the performance.

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Abbreviations

RCTC:

Robust Computed Torque Control

BASMC:

Boundary Augmented Sliding Mode Control

RSMC:

Robust Sliding Mode Control

ULRR:

Upper Limb Rehabilitation Robot

References

  1. Mozaffarian, D., Benjamin, E. J., Go, A. S., Arnett, D. K., Blaha, M. J., et al., “Executive Summary: Heart Disease and Stroke Statistics-2015 Update: A Report from the American Heart Association,” Circulation, Vol. 131, No. 4, pp. 434, 2015.

    Article  Google Scholar 

  2. Barreca, S., Wolf, S. L., Fasoli, S., and Bohannon, R., “Treatment Interventions for the Paretic Upper Limb of Stroke Survivors: A Critical Review,” Neurorehabilitation and Neural Repair, Vol. 17, No. 4, pp. 220–226, 2003.

    Article  Google Scholar 

  3. Feys, H. M., De Weerdt, W. J., Selz, B. E., Steck, G. A. C., Spichiger, R., et al., “Effect of a Therapeutic Intervention for the Hemiplegic Upper Limb in the Acute Phase after Stroke a Single-Blind, Randomized, Controlled Multicenter Trial,” Stroke, Vol. 29, No. 4, pp. 785–792, 1998.

    Article  Google Scholar 

  4. Kwakkel, G., Wagenaar, R. C., Twisk, J. W., Lankhorst, G. J., and Koetsier, J. C., “Intensity of Leg and Arm Training after Primary Middle-Cerebral-Artery Stroke: A Randomised Trial,” The Lancet, Vol. 354, No. 9174, pp. 191–196, 1999.

    Article  Google Scholar 

  5. Zhang, J. and Cheah, C. C., “Passivity and Stability of Human-Robot Interaction Control for Upper-Limb Rehabilitation Robots,” IEEE Transactions on Robotics, Vol. 31, No. 2, pp. 233–245, 2015.

    Article  Google Scholar 

  6. Choi, J. H., Shin, D. H., Park, T. S., Jeong, C. P., Moon, J. I., and An, J. N., “Kinematic Design Consideration based on Actuator Placement of Five-Bar Planar Robot for Arm Rehabilitation,” Key Engineering Materials, Vol. 625, pp. 638–643, 2014.

    Article  Google Scholar 

  7. Martin, P. and Emami, M. R., “A Neuro-Fuzzy Approach to Real-Time Trajectory Generation for Robotic Rehabilitation,” Robotics and Autonomous Systems, Vol. 62, No. 4, pp. 568–578, 2014.

    Article  Google Scholar 

  8. Rahman, M. H., Saad, M., Kenn, J.-P., and Archambault, P. S., “Control of an Exoskeleton Robot Arm with Sliding Mode Exponential Reaching Law,” International Journal of Control, Automation and Systems, Vol. 11, No. 1, pp. 92–104, 2013.

    Article  Google Scholar 

  9. Ueda, J., Ming, D., Krishnamoorthy, V., Shinohara, M., and Ogasawara, T., “Individual Muscle Control using an Exoskeleton Robot for Muscle Function Testing,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 18, No. 4, pp. 339–350, 2010.

    Article  Google Scholar 

  10. Ugurlu, B., Nishimura, M., Hyodo, K., Kawanishi, M., and Narikiyo, T., “Proof of Concept for Robot-Aided Upper Limb Rehabilitation using Disturbance Observers,” IEEE Transactions on Human-Machine Systems, Vol. 45, No. 1, pp. 110–118, 2015.

    Article  Google Scholar 

  11. Wu, C., Song, A., Li, H., Xu, B., Xu, X., et al., “Upper Limb Rehabilitation Training Robot and Its Control Method,” Chinese Journal of Scientific Instrument, Vol. 35, No. 5, pp. 999–1004, 2014.

    Google Scholar 

  12. Kwakkel, G., Kollen, B. J., and Krebs, H. I., “Effects of Robot-Assisted Therapy on Upper Limb Recovery after Stroke: A Systematic Review,” Neurorehabilitation and Neural Repair, DOI No. 10.1177/1545968307305457, 2007.

    Google Scholar 

  13. Lee, H., Kim, W., Han, J., and Han, C., “The Technical Trend of the Exoskeleton Robot System for Human Power Assistance,” Int. J. Precis. Eng. Manuf., Vol. 13, No. 8, pp. 1491–1497, 2012.

    Article  Google Scholar 

  14. Kim, H.-M. and Kim, G.-S., “Development of a Finger-Rehabilitation Robot for Fingers’ Flexibility Rehabilitation Exercise,” Int. J. Precis. Eng. Manuf., Vol. 14, No. 4, pp. 535–541, 2013.

    Article  Google Scholar 

  15. Kim, J.-Y., Yang, U.-J., and Park, K., “Design, Motion Planning and Control of Frozen Shoulder Rehabilitation Robot,” Int. J. Precis. Eng. Manuf., Vol. 15, No. 9, pp. 1875–1881, 2014.

    Article  Google Scholar 

  16. Babaiasl, M., Mahdioun, S. H., Jaryani, P., and Yazdani, M., “A Review of Technological and Clinical Aspects of Robot-Aided Rehabilitation of Upper-Extremity after Stroke,” Disability and Rehabilitation: Assistive Technology, DOI No. 10.3109/17483107. 2014.1002539, 2015.

    Google Scholar 

  17. Nef, T., Mihelj, M., and Riener, R., “Armin: A Robot for Patient-Cooperative Arm Therapy,” Medical & Biological Engineering & Computing, Vol. 45, No. 9, pp. 887–900, 2007.

    Article  Google Scholar 

  18. Yu, W. and Rosen, J., “A Novel Linear Pid Controller for an Upper Limb Exoskeleton,” Proc. of 49th IEEE Conference on Decision and Control (CDC), pp. 3548–3553, 2010.

    Chapter  Google Scholar 

  19. Rahman, M., Rahman, M., Cristobal, O., Saad, M., Kenn, J., and Archambault, P., “Development of a Whole Arm Wearable Robotic Exoskeleton for Rehabilitation and to Assist Upper Limb Movements,” Robotica, Vol. 33, No. 1, pp. 19–39, 2015.

    Article  Google Scholar 

  20. Spong, M. W., Hutchinson, S., and Vidyasagar, M., “Robot Modeling and Control,” Wiley New York, pp. 289–311, 2006.

    Google Scholar 

  21. Hill, J. and Fahimi, F., “Active Disturbance Rejection for Walking Bipedal Robots using the Acceleration of the Upper Limbs,” Robotica, Vol. 33, No. 2, pp. 264–281, 2015.

    Article  Google Scholar 

  22. Guga, J., “Cyborg Tales: The Reinvention of the Human in the Information Age,” Beyond Artificial Intelligence, Vol. 9, pp. 45–62, 2015.

    Google Scholar 

  23. Jiang, X., Wang, Z., Zhang, C., and Yang, L., “Fuzzy Neural Network Control of the Rehabilitation Robotic Arm Driven by Pneumatic Muscles,” Industrial Robot: An International Journal, Vol. 42, No. 1, pp. 36–43, 2015.

    Article  Google Scholar 

  24. Otten, A., Voort, C., Stienen, A., Aarts, R., van Asseldonk, E., and Kooij, H., “LIMPACT: A Hydraulically Powered Self-Aligning Upper Limb Exoskeleton,” IEEE/ASME Transactions on Mechatronics, Vol. 20, No. 5, pp. 2285–4435, 2015.

    Article  Google Scholar 

  25. Slotine, J.-J. E. and Li, W., “Applied Nonlinear Control,” Prentice-Hall Englewood Cliffs, pp. 276–299, 1991.

    Google Scholar 

  26. Wicke, J. and Dumas, G. A., “A New Geometric-based Model to Accurately Estimate Arm and Leg Inertial Estimates,” Journal of Biomechanics, Vol. 47, No. 8, pp. 1869–1875, 2014.

    Article  Google Scholar 

  27. Winter, D. A., “Biomechanics and Motor Control of Human Movement,” John Wiley & Sons, pp. 82–103, 2009.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Hanyang University, 222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, South Korea

    Deokwon Yun & Hyeyoun Jang

  2. Department of Mechanical Design Engineering, Hanyang University, 55, Hanyangdaehak-ro, Sangnok-gu, Ansan-si, Gyeonggi-do, 15588, South Korea

    Abdul Manan Khan

  3. Department of Mechatronics Engineering, Hanyang University, 55, Hanyangdaehak-ro, Sangnok-gu, Ansan-si, Gyeonggi-do, 15588, South Korea

    Rui-Jun Yan, Younghoon Ji, Junaid Iqbal & K. M. Zuhaib

  4. Department of Orthopaedic Surgery, Cheil General Hospital and Womens Healthcare Center, 17, Seoae-ro 1-gil, Jung-gu, Seoul, 04619, South Korea

    Jae Yong Ahn

  5. Department of Mechanical Systems Engineering, Hansung University, 116, Samseongyo-ro 16-gil, Seongbuk-gu, Seoul, 02876, South Korea

    Jungsoo Han

  6. Department of Robot Engineering, Hanyang University, 55, Hanyangdaehak-ro, Sangnok-gu, Ansan-si, Gyeonggi-do, 15588, South Korea

    Changsoo Han

Authors
  1. Deokwon Yun
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  2. Abdul Manan Khan
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  3. Rui-Jun Yan
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  4. Younghoon Ji
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  5. Hyeyoun Jang
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  6. Junaid Iqbal
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  7. K. M. Zuhaib
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  8. Jae Yong Ahn
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  9. Jungsoo Han
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  10. Changsoo Han
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Corresponding author

Correspondence to Changsoo Han.

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Yun, D., Khan, A.M., Yan, RJ. et al. Handling subject arm uncertainties for upper limb rehabilitation robot using robust sliding mode control. Int. J. Precis. Eng. Manuf. 17, 355–362 (2016). https://doi.org/10.1007/s12541-016-0044-6

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  • DOI: https://doi.org/10.1007/s12541-016-0044-6

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