Advertisement

Passivity based adaptive control for upper extremity assist exoskeleton

  • Abdul Manan Khan
  • Deok-won Yun
  • Mian Ashfaq Ali
  • Khalil Muhammad Zuhaib
  • Chao Yuan
  • Junaid Iqbal
  • Jungsoo Han
  • Kyoosik Shin
  • Changsoo Han
Article

Abstract

Upper limb assist exoskeleton robot requires quantitative techniques to assess human motor function and generate command signal for robots to act in compliance with human motion. To asses human motor function, we present Desired Motion Intention (DMI) estimation algorithm using Muscle Circumference Sensor (MCS) and load cells. Here, MCS measures human elbow joint torque using human arm kinematics, biceps/triceps muscle model and physiological cross sectional area of these muscles whereas load cells play a compensatory role for the torque generated by shoulder muscles as these cells measure desire of shoulder muscles to move the arm and not the internal activity of shoulder muscles. Furthermore, damped least square algorithm is used to estimate Desired Motion Intention (DMI) from these torques. To track this estimated DMI, we have used passivity based adaptive control algorithm. This control techniques is particular useful to adapt modeling error of assist exoskeleton robot for different subjects. Proposed methodology is experimentally evaluated on seven degree of freedom upper limb assist exoskeleton. Results show that DMI is well estimated and tracked for assistance by the proposed control algorithm.

Keywords

Adaptive control human robot interaction passivity based robot control 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    J. L. Pons, Wearable Robots: Biomechatronic Exoskeletons, 2008. [click]CrossRefGoogle Scholar
  2. [2]
    M. Rahman, M. Saad, J.-P. Kenné, and P. Archambault, “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. [click]CrossRefGoogle Scholar
  3. [3]
    Ispointorg, “International society for prosthetics and orthotics.” http://wwwispointorg/. Accessed: 2014-06-15.Google Scholar
  4. [4]
    D. Choi and J.-h. Oh, “Development of the cartesian arm exoskeleton system (caes) using a 3-axis force/torque sensor,” International Journal of Control, Automation and Systems, vol. 11, no. 5, pp. 976–983, 2013. [click]CrossRefGoogle Scholar
  5. [5]
    A. Gupta and M. O’Malley, “Design of a haptic arm exoskeleton for training and rehabilitation,” IEEE/ASME Transactions on Mechatronics, vol. 11, no. 3, pp. 280–289, 2006. [click]CrossRefGoogle Scholar
  6. [6]
    K. Kong and D. Jeon, “Design and control of an exoskeleton for the elderly and patients,” IEEE/ASME Transactions on Mechatronics, vol. 11, pp. 428–432, Aug 2006. [click]CrossRefGoogle Scholar
  7. [7]
    B. Dellon and Y. Matsuoka, “Prosthetics, exoskeletons, and rehabilitation [grand challenges of robotics],” IEEE Robotics and Automation Magazine, vol. 14, no. 1, pp. 30–34, 2007. [click]CrossRefGoogle Scholar
  8. [8]
    C.-J. Yang, J.-F. Zhang, Y. Chen, Y.-M. Dong, and Y. Zhang, “A review of exoskeleton-type systems and their key technologies,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 222, no. 8, pp. 1599–1612, 2008. [click]Google Scholar
  9. [9]
    R. Bogue, “Robots to aid the disabled and the elderly,” Industrial Robot, vol. 40, no. 6, pp. 519–524, 2013. [click]CrossRefGoogle Scholar
  10. [10]
    Z. Mohamed, M. Kitani, S.-i. Kaneko, and G. Capi, “Humanoid robot arm performance optimization using multi objective evolutionary algorithm,” International Journal of Control, Automation and Systems, vol. 12, no. 4, pp. 870–877, 2014. [click]CrossRefGoogle Scholar
  11. [11]
    H.-g. Kim, J.-w. Lee, J. Jang, S. Park, and C. Han, “Design of an exoskeleton with minimized energy consumption based on using elastic and dissipative elements,” International Journal of Control, Automation and Systems, vol. 13, no. 2, pp. 463–474, 2015. [click]CrossRefGoogle Scholar
  12. [12]
    M. A. Mikulski, “Electromyogram control algorithms for the upper limb single-dof powered exoskeleton,” Proc. of 4th International Conference on Human System Interaction, HSI 2011, pp. 117–122, 2011. [click]Google Scholar
  13. [13]
    J. Gunasekara, R. Gopura, T. Jayawardane, and S. Lalitharathne, “Control methodologies for upper limb exoskeleton robots,” Proc. of IEEE/SICE International Symposium on System Integration (SII), pp. 19–24, Dec 2012. [click]Google Scholar
  14. [14]
    K. Kiguchi and Y. Hayashi, “An emg-based control for an upper-limb power-assist exoskeleton robot,” Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 42, pp. 1064–1071, aug. 2012. [click]CrossRefGoogle Scholar
  15. [15]
    J. Ueda, D. Ming, V. Krishnamoorthy, M. Shinohara, and T. Ogasawara, “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. [click]CrossRefGoogle Scholar
  16. [16]
    W. Huo, J. Huang, Y. Wang, J. Wu, and L. Cheng, “Control of upper-limb power-assist exoskeleton based on motion intention recognition,” in Robotics and Automation (ICRA), 2011 IEEE International Conference on, pp. 2243–2248, may 2011. [click]Google Scholar
  17. [17]
    W. Kim, H. Lee, D. Lim, J. Han, K. Shin, and C. Han, “Development of a muscle circumference sensor to estimate torque of the human elbow joint,” Sensors and Actuators A: Physical, vol. 208, no. 0, pp. 95–103, 2014. [click]CrossRefGoogle Scholar
  18. [18]
    S. Jlassi, S. Tliba, and Y. Chitour, “An event-controlled online trajectory generator based on the human-robot interaction force processing,” Industrial Robot, vol. 41, no. 1, pp. 15–25, 2014. [click]CrossRefGoogle Scholar
  19. [19]
    M. Babaiasl, S. H. Mahdioun, P. Jaryani, and M. Yazdani, “A review of technological and clinical aspects of robot-aided rehabilitation of upper-extremity after stroke,” Disability and Rehabilitation: Assistive Technology, no. preprint, pp. 1–18, 2015. [click]Google Scholar
  20. [20]
    T. Nef, M. Mihelj, and R. Riener, “Armin: a robot for patient-cooperative arm therapy,” Medical & Bological Engineering & Computing, vol. 45, no. 9, pp. 887–900, 2007. [click]CrossRefGoogle Scholar
  21. [21]
    W. Yu and J. Rosen, “A novel linear pid controller for an upper limb exoskeleton,” Proc. of 49th IEEE Conference on Decision and Control (CDC), pp. 3548–3553, IEEE, 2010. [click]CrossRefGoogle Scholar
  22. [22]
    M. W. Spong, S. Hutchinson, and M. Vidyasagar, Robot Modeling and Control, Willey, Upper Saddle River, NJ, New York, 2006.Google Scholar
  23. [23]
    J. Hill and F. Fahimi, “Active disturbance rejection for walking bipedal robots using the acceleration of the upper limbs,” Robotica, vol. 33, no. 02, pp. 264–281, 2015. [click]CrossRefGoogle Scholar
  24. [24]
    J. Guga, “Cyborg tales: The reinvention of the human in the information age,” in Beyond Artificial Intelligence, pp. 45–62, Springer, 2015. [click]Google Scholar
  25. [25]
    X. Jiang, Z. Wang, C. Zhang, and L. Yang, “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. [click]CrossRefGoogle Scholar
  26. [26]
    B. Siciliano, L. Sciavicco, L. Villani, and G. Oriolo, Robotics Modelling, Planning and Control, Springer, Upper Saddle River, NJ, 2008. [click]Google Scholar
  27. [27]
    D. Winter, Biomechanics and Motor Control of Human Movement, 4th ed., Wiely, 2009. [click]CrossRefGoogle Scholar
  28. [28]
    D. F. B. Haeufle, M. Günther, A. Bayer, and S. Schmitt, “Hill-type muscle model with serial damping and eccentric force-velocity relation,” Journal of Biomechanics, vol. 47, no. 6, pp. 1531–1536, 2014. [click]9CrossRefGoogle Scholar
  29. [29]
    P. Pigeon, L. Yahia, and A. G. Feldman, “Moment arms and lengths of human upper limb muscles as functions of joint angles,” Journal of Biomechanics, vol. 29, no. 10, pp. 1365–1370, 1996. [click]CrossRefGoogle Scholar
  30. [30]
    H. Lee, B. Lee, W. Kim, M. Gil, J. Han, and C. Han, “Human-robot cooperative control based on phri (physical human-robot interaction) of exoskeleton robot for a human upper extremity,” International Journal of Precision Engineering and Manufacturing, vol. 13, no. 6, pp. 985–992, 2012. [click]CrossRefGoogle Scholar
  31. [31]
    J. Wicke and G. Dumas, “A new geometric-based model to accurately estimate arm and leg inertial estimates,” Journal of Biomechanics, vol. 47, no. 8, pp. 1869–1875, 2014. [click]CrossRefGoogle Scholar
  32. [32]
    R. Riener, L. Lunenburger, S. Jezernik, M. Anderschitz, G. Colombo, and V. Dietz, “Patient-cooperative strategies for robot-aided treadmill training: first experimental results,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 13, no. 3, pp. 380–394, 2005. [click]CrossRefGoogle Scholar

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Abdul Manan Khan
    • 1
  • Deok-won Yun
    • 2
  • Mian Ashfaq Ali
    • 3
  • Khalil Muhammad Zuhaib
    • 3
  • Chao Yuan
    • 3
  • Junaid Iqbal
    • 3
  • Jungsoo Han
    • 4
  • Kyoosik Shin
    • 5
  • Changsoo Han
    • 5
  1. 1.Department of Mechanical Design EngineeringHanyang UniversitySeoulKorea
  2. 2.Department of Mechanical EngineeringHanyang UniversitySeoulKorea
  3. 3.Department of Mechatornics EngineeringHanyang UniversityAnsanKorea
  4. 4.Department of Mechanical System EngineeringHansung UniversitySeoulKorea
  5. 5.Department of Robot EngineeringHanyang UniversityAnsanKorea

Personalised recommendations