Estimation of Desired Motion Intention and compliance control for upper limb assist exoskeleton

  • Abdul Manan Khan
  • Deok-won Yun
  • Khalil Muhammad Zuhaib
  • Junaid Iqbal
  • Rui-Jun Yan
  • Fatima Khan
  • Changsoo Han
Regular Papers Robot and Applications
  • 164 Downloads

Abstract

In this paper, we have addressed two issues for upper limb assist exoskeleton. 1) Estimation of Desired Motion Intention (DMI); 2) Robust compliance control. To estimate DMI, we have employed Extreme Learning Machine Algorithm. This algorithm is free from traditional Neural Network based problems such as local minima, selection of suitable parameters, slow convergence of adaptation law and over-fitting. These problems cause lot of problem in tuning the intelligent algorithm for the desired results. Furthermore, to track the estimated trajectory, we have developed model reference based adaptive impedance control algorithm. This control algorithm is based on stable poles of desired impedance model, forcing the over all system to act as per desired impedance model. It also considers robot and human model uncertainties. To highlight the effectiveness of the proposed control algorithm, we have compared it with simple impedance and target reference based impedance control algorithms. Experimental evaluation is carried on seven degree of freedom upper limb assist exoskeleton. Results describe the effectiveness of ELM algorithm for DMI estimation and robust tracking of the estimated trajectory by the proposed model reference adaptive impedance control law.

Keywords

Extreme learning machine human motion intention estimation model reference adaptive impedance control upper limb assist exoskeleton 

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References

  1. [1]
    J. Xu, K. D. Kochanek, S. L. Murphy, and E. Arias, “Mortality in the united states, 2012,” NCHS data brief, no. 168, pp. 1–8, 2014.Google Scholar
  2. [2]
    A. Calanca and P. Fiorini, “Human-adaptive control of series elastic actuators,” Robotica, vol. 32, no. 8, pp. 1301–1316, 2014. [click]CrossRefGoogle Scholar
  3. [3]
    I. H. Ertas, E. Hocaoglu, and V. Patoglu, “Assist On-Finger: An under-actuated finger exoskeleton for robotassisted tendon therapy,” Robotica, vol. 32, no. 08, pp. 1363–1382, 2014. [click]CrossRefGoogle Scholar
  4. [4]
    A. M. Khan, D.-w. Yun, M. A. Ali, K. M. Zuhaib, C. Yuan, J. Iqbal, J. Han, K. Shin, and C. Han, “Passivity based adaptive control for upper extremity assist exoskeleton,” International Journal of Control, Automation and Systems, vol. 14, no. 1, pp. 291–300, 2016. [click]CrossRefGoogle Scholar
  5. [5]
    H.-Y. Jang, Y.-H. Ji, J.-S. Han, A. Khan, J.-Y. Ahn, and C.-S. Han, “Development and verification of upper extremities wearable robots to aid muscular strength with the optimization of link parameters,” International Journal of Precision Engineering and Manufacturing, vol. 16, no. 12, pp. 2569–2575, 2015. [click]CrossRefGoogle Scholar
  6. [6]
    R. Lu, Z. Li, C.-Y. Su, and A. Xue, “Development and learning control of a human limb with a rehabilitation exoskeleton,” IEEE Transactions on Industrial Electronics, vol. 61, no. 7, pp. 3776–3785, 2014.CrossRefGoogle Scholar
  7. [7]
    H. Lo and S. Xie, “Exoskeleton robots for upper-limb rehabilitation: State of the art and future prospects,” Medical Engineering and Physics, vol. 34, no. 3, pp. 261–268, 2012.CrossRefGoogle Scholar
  8. [8]
    H.-B. Kang and J.-H. Wang, “Adaptive robust control of 5 dof upper-limb exoskeleton robot,” International Journal of Control, Automation and Systems, vol. 13, no. 3, pp. 733–741, 2015. [click]CrossRefGoogle Scholar
  9. [9]
    P. Maciejasz, J. Eschweiler, K. Gerlach-Hahn, A. Jansen-Troy, and S. Leonhardt, “A survey on robotic devices for upper limb rehabilitation,” Journal of neuroengineering and rehabilitation, vol. 11, no. 1, p. 3, 2014.CrossRefGoogle Scholar
  10. [10]
    M. A. Mikulski, “Electromyogram control algorithms for the upper limb single-dof powered exoskeleton,” Proc. of 4th International Conference on Human System Interaction, pp. 117–122, 2011.Google Scholar
  11. [11]
    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.Google Scholar
  12. [12]
    A. Khan, D.-W. Yun, M. Ali, J. Han, K. Shin, and C. Han, “Adaptive impedance control for upper limb assist exoskeleton,” Proc. of IEEE International Conference on Robotics and Automation (ICRA), pp. 4359–4366, 2015.Google Scholar
  13. [13]
    H.-D. Lee, B.-K. Lee, W.-S. Kim, J.-S. Han, K.-S. Shin, and C.-S. Han, “Human-robot cooperation control based on a dynamic model of an upper limb exoskeleton for human power amplification,” Mechatronics, vol. 24, no. 2, pp. 168–176, 2014. [click]CrossRefGoogle Scholar
  14. [14]
    E. Burdet and T. E. Milner, “Quantization of human motions and learning of accurate movements,” Biological cybernetics, vol. 78, no. 4, pp. 307–318, 1998. [click]CrossRefMATHGoogle Scholar
  15. [15]
    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
  16. [16]
    Z. Wang, A. Peer, and M. Buss, “An hmm approach to realistic haptic human-robot interaction,” Proc. of 3rd Joint EuroHaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, World Haptics 2009, pp. 374–379, 2009.CrossRefGoogle Scholar
  17. [17]
    K. Wakita, J. Huang, P. Di, K. Sekiyama, and T. Fukuda, “Human-walking-intention-based motion control of an omnidirectional-type cane robot,” IEEE/ASME Transactions on Mechatronics, vol. 18, no. 1, pp. 285–296, 2013.CrossRefGoogle Scholar
  18. [18]
    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
  19. [19]
    P. H. Chang, K. Park, S. H. Kang, H. I. Krebs, and N. Hogan, “Stochastic estimation of human arm impedance using robots with nonlinear frictions: An experimental validation,” IEEE/ASME Transactions on Mechatronics, vol. 18, no. 2, pp. 775–786, 2013.CrossRefGoogle Scholar
  20. [20]
    S. S. Ge, Y. Li, and H. He, “Neural-network-based human intention estimation for physical human-robot interaction,” Proc. of 8th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), pp. 390–395, 2011.Google Scholar
  21. [21]
    K. Kiguchi and Y. Hayashi, “An emg-based control for an upper-limb power-assist exoskeleton robot,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 42, no. 4, pp. 1064–1071, 2012.CrossRefGoogle Scholar
  22. [22]
    Y. Li and S. S. Ge, “Human-robot collaboration based on motion intention estimation,” IEEE/ASME Transactions on Mechatronics, vol. 19, no. 3, pp. 1007–1014, 2014.CrossRefGoogle Scholar
  23. [23]
    G. Huang, G.-B. Huang, S. Song, and K. You, “Trends in extreme learning machines: A review,” Neural Networks, vol. 61, pp. 32–48, 2015. [click]CrossRefMATHGoogle Scholar
  24. [24]
    C. Deng, G. Huang, J. Xu, and J. Tang, “Extreme learning machines: new trends and applications,” Science China Information Sciences, vol. 58, no. 2, pp. 1–16, 2015.CrossRefGoogle Scholar
  25. [25]
    Z. Li, B. Wang, F. Sun, C. Yang, Q. Xie, and W. Zhang, “semg-based joint force control for an upper-limb powerassist exoskeleton robot,” IEEE Journal of Biomedical and Health Informatics, vol. 18, pp. 1043–1050, May 2014.CrossRefGoogle Scholar
  26. [26]
    T. Lalitharatne, K. Teramoto, Y. Hayashi, and K. Kiguchi, “Evaluation of perception-assist with an upper-limb powerassist exoskeleton using emg and eeg signals,” Proc. of the 11th IEEE International Conference on Networking, Sensing and Control, ICNSC 2014, pp. 524–529, 2014.Google Scholar
  27. [27]
    M. Rahman, M. Rahman, O. Cristobal, M. Saad, J. Kenné, and P. Archambault, “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. [click]CrossRefGoogle Scholar
  28. [28]
    W. S. Newman, “Stability and performance limits of interaction controllers,” Journal of Dynamic Systems, Measurement, and Control, vol. 114, no. 4, pp. 563–570, 1992. [click]CrossRefMATHGoogle Scholar
  29. [29]
    P. Pitakwatchara, “Task space impedance control of the manipulator driven through the multistage nonlinear flexible transmission,” Journal of Dynamic Systems, Measurement, and Control, vol. 137, no. 2, p. 021001, 2015.CrossRefGoogle Scholar
  30. [30]
    R. Ozawa, H. Kobayashi, and R. Ishibashi, “Adaptive impedance control of a variable stiffness actuator,” Advanced Robotics, vol. 29, no. 4, pp. 273–286, 2015. [click]CrossRefGoogle Scholar
  31. [31]
    B. Adorno, A. Bó, and P. Fraisse, “Kinematic modeling and control for human-robot cooperation considering different interaction roles,” Robotica, vol. 33, no. 02, pp. 314–331, 2015. [click]CrossRefGoogle Scholar
  32. [32]
    A. M. Khan, D.-w. Yun, J.-S. Han, K. Shin, and C.-S. Han, “Upper extremity assist exoskeleton robot,” Proc. of 23rd IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 892–898, 2014.CrossRefGoogle Scholar
  33. [33]
    D. Yun, A. Khan, R.-J. Yan, Y. Ji, H. Jang, J. Iqbal, K. Zuhaib, J. Ahn, J. Han, and C. Han, “Handling subject arm uncertainties for upper limb rehabilitation robot using robust sliding mode control,” International Journal of Precision Engineering and Manufacturing, vol. 17, no. 3, pp. 355–362, 2016. [click]CrossRefGoogle Scholar
  34. [34]
    N. Hogan, “Impedance control: An approach to manipulation: Part I:-theory,” Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, vol. 107, no. 1, pp. 1–7, 1985.CrossRefMATHGoogle Scholar
  35. [35]
    N. Hogan, “Impedance control: An approach to manipulation: Part II-implementation,” Dynamic Systems, Measurement, and Control, vol. 107, no. 1, pp. 8–16, 1985.CrossRefMATHGoogle Scholar
  36. [36]
    N. Hogan, “Impedance control: An approach to manipulation: Part III-applications,” Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, vol. 107, no. 1, pp. 17–24, 1985.CrossRefMATHGoogle Scholar
  37. [37]
    R. Anderson and M. Spong, “Hybrid impedance control of robotic manipulators,” IEEE Journal of Robotics and Automation, vol. 4, pp. 549–556, Oct 1988.CrossRefGoogle Scholar
  38. [38]
    M. W. Spong, S. Hutchinson, and M. Vidyasagar, Robot Modeling and Control, vol. 3. Wiley New York, 2006.Google Scholar
  39. [39]
    H. Kazerooni, T. Sheridan, and P. Houpt, “Robust compliant motion for manipulators, part I: The fundamental concepts of compliant motion,” IEEE Journal on Robotics and Automation, vol. 2, no. 2, pp. 83–92, 1986.CrossRefGoogle Scholar
  40. [40]
    H. Kazerooni, P. Houpt, and T. Sheridan, “Robust compliant motion for manipulators-II: Design method,” IEEE Journal of Robotics and Automation, vol. 2, no. 2, pp. 93–105, 1986.CrossRefGoogle Scholar
  41. [41]
    A. Hace, K. Jezernik, and S. Uran, “Robust impedance control,” Proc. of the IEEE International Conference on Control Applications, vol. 1, pp. 583–587 vol. 1, Sep 1998.Google Scholar
  42. [42]
    W.-S. Lu and Q.-H. Meng, “Impedance control with adaptation for robotic manipulations,” IEEE Transactions on Robotics and Automation, vol. 7, pp. 408–415, Jun 1991.CrossRefGoogle Scholar
  43. [43]
    M.-C. Chien and A.-C. Huang, “Adaptive impedance control of robot manipulators based on function approximation technique,” Robotica, vol. 22, pp. 395–403, 8 2004. [click]CrossRefGoogle Scholar
  44. [44]
    H. Seraji, “Adaptive admittance control: an approach to explicit force control in compliant motion,” Proc. of IEEE International Conference on Robotics and Automation (ICRA), pp. 2705–2712 vol. 4, May 1994.Google Scholar
  45. [45]
    K. P. Tee, R. Yan, and H. Li, “Adaptive admittance control of a robot manipulator under task space constraint,” Proc. of IEEE International Conference on Robotics and Automation (ICRA), pp. 5181–5186, May 2010.Google Scholar
  46. [46]
    Z. Soitrov and R. Botev, “A model reference approach to adaptive impedance control of robot manipulators,” Proc. of the 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 2, pp. 727–733 vol. 2, Jul 1993.CrossRefGoogle Scholar
  47. [47]
    K. Wedeward and R. Colbaugh, “New stability results for direct adaptive impedance control,” Proc. of the IEEE International Symposium on Intelligent Control, pp. 281–287, Aug 1995.CrossRefGoogle Scholar
  48. [48]
    J.-J. E. Slotine and W. Li, Applied Nonlinear Control, Pearson, Upper Saddle River, NJ, 1991.MATHGoogle Scholar
  49. [49]
    Y. Li and S. Ge, “Human-robot collaboration based on motion intention estimation,” IEEE/ASME Transactions on Mechatronics, vol. 19, no. 3, pp. 1007–1014, 2014.CrossRefGoogle Scholar
  50. [50]
    E. Wolbrecht, V. Chan, D. Reinkensmeyer, and J. Bobrow, “Optimizing compliant, model-based robotic assistance to promote neurorehabilitation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 16, pp. 286–297, June 2008.CrossRefGoogle Scholar
  51. [51]
    D. A. Winter, Biomechanics and motor control of human movement, John Wiley & Sons, 2009.CrossRefGoogle Scholar
  52. [52]
    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

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Abdul Manan Khan
    • 1
  • Deok-won Yun
    • 1
  • Khalil Muhammad Zuhaib
    • 2
  • Junaid Iqbal
    • 2
  • Rui-Jun Yan
    • 3
  • Fatima Khan
    • 4
  • Changsoo Han
    • 5
  1. 1.Department of Robot EngineeringHanyang UniversityGyeonggi-doKorea
  2. 2.Department of Mechatronics EngineeringHanyang UniversityAnsanKorea
  3. 3.School of Mechanical and Aerospace EngineeringNanyang Technological UniversitySingaporeSingapore
  4. 4.Department of PhysiotherapyCMH LahoreLahorePakistan
  5. 5.Department of Robot EngineeringHanyang UniversityAnsanKorea

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