Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-23T17:42:44.320Z Has data issue: false hasContentIssue false
  • English
  • Français

Variable admittance control of the exoskeleton for gait rehabilitation based on a novel strength metric

Published online by Cambridge University Press:  20 November 2017

Ali Taherifar ,
Gholamreza Vossoughi  and
Ali Selk Ghafari
Ali Taherifar
Affiliation:
School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran
Gholamreza Vossoughi*
Affiliation:
School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran
Ali Selk Ghafari
Affiliation:
School of Science and Engineering, Sharif University of Technology, International Campus, Kish Island, Iran
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Assist-as-needed control is underlain by the aim of replacing skillful therapists with rehabilitation robots. The objective of this research was to introduce a smart assist-as-needed control system for the elderly or partially paralyzed individuals. The main function of the proposed system is to assist the patients just in the required sub phases of the motion. To ensure that a smart and compliant system is developed, the target admittance gains of the controller was adapted according to the concept of energy The admittance gains were modified so that an exoskeleton reduces interaction energy in cases wherein users have sufficient strength for task execution and maximizes the interaction energy in the required subphases. The results of simulations and an experimental investigation on a novel exoskeleton showed that the proposed adaptive admittance control improves performance to a level substantially higher than that achieved with constant impedance control.

Keywords

Adaptive admittance controlExoskeletonAssist-as-needed controlHuman–robot interactionStrength metric
Type
Articles
Information
Robotica , Volume 36 , Issue 3 , March 2018 , pp. 427 - 447
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Riener, R., Lunenburger, L., Jezernik, S., Anderschitz, M., Colombo, G. and Dietz, V., “Patient-cooperative strategies for robot-aided treadmill training: first experimental results,” IEEE Trans. Neural Syst. Rehabil. Eng. 13 (3), 380–394 (2005).Google Scholar
2. Emken, J. L., Bobrow, J. E. and Reinkensmeyer, D. J., “Robotic Movement Training as an Optimization Problem: Designing a Controller that Assists only as Needed,” Proceedings of the Rehabilitation Robotics, 2005. ICORR 2005. 9th International Conference on IEEE (2005) pp. 307–312.Google Scholar
3. Han, Y., Zhu, S., Zhou, Z., Shi, Y. and Hao, D., “Research on a multimodal actuator-oriented power-assisted knee exoskeleton,” Robotica 35 (9), 1906–1922 (2016).Google Scholar
4. Cai, L. L. et al., “Implications of assist-as-needed robotic step training after a complete spinal cord injury on intrinsic strategies of motor learning,” J. Neuroscience 26 (41), 10564–10568 (2006).Google Scholar
5. Emken, J. L., Harkema, S. J., Beres-Jones, J., Ferreira, C. K. and Reinkensmeyer, D. J., “Feasibility of manual teach-and-replay and continuous impedance shaping for robotic locomotor training following spinal cord injury,” IEEE Trans. Biomed. Eng. 55 (1), 322–334 (2008).CrossRefGoogle ScholarPubMed
6. Wolbrecht, E. T., Chan, V., Reinkensmeyer, D. J. and Bobrow, J. E., “Optimizing compliant, model-based robotic assistance to promote neurorehabilitation,” IEEE Trans. Neural Syst. Rehabil. Eng. 16 (3), 286–297 (2008).Google Scholar
7. Oboe, R. and Pilastro, D., “Non-linear Adaptive Impedance Controller for Rehabilitation Purposes,” Proceedings of the Advanced Motion Control (AMC), 2014 IEEE 13th International Workshop on IEEE, (2014) pp. 272–277.Google Scholar
8. Hussain, S., Jamwal, P. K., Ghayesh, M. H. and Xie, S. Q., “Assist-as-needed control of an intrinsically compliant robotic gait training orthosis,” IEEE Trans. Ind. Electron. 64 (2), 1675–1685 (2017).Google Scholar
9. Burdet, E., Ganesh, G., Yang, C. and Albu-Schäffer, A., “Interaction Force, Impedance and Trajectory Adaptation: By Humans, for Robots,” In: Experimental Robotics (Khatib, O., Kumar, V. and Sukhatme, G., eds.), Springer Tracts in Advanced Robotics, vol. 79, (Springer, Berlin, Heidelberg, 2014) pp. 331–345.CrossRefGoogle Scholar
10. Mehdi, H. and Boubaker, O., “Stiffness and impedance control using Lyapunov theory for robot-aided rehabilitation,” Int. J. Social Robot. 4 (1), 107–119 (2012).Google Scholar
11. Li, Y., Sam Ge, S. and Yang, C., “Learning impedance control for physical robot–environment interaction,” Int. J. Control 85 (2), 182–193 (2012).Google Scholar
12. Chien, M.-C. and Huang, A.-C., “Adaptive impedance control of robot manipulators based on function approximation technique,” Robotica 22 (04), 395–403 (2004).Google Scholar
13. Winter, D. A., Biomechanics and Motor Control of Human Movement. (Wiley, 2009).CrossRefGoogle Scholar
14. Hogan, N., “Impedance control: An approach to manipulation: Part III applications,” J. Dynamic Syst., Meas. Control 107 (2), 17 (1985).Google Scholar
15. Oh, S., Baek, E., Song, S.-k., Mohammed, S., Jeon, D. and Kong, K., “A generalized control framework of assistive controllers and its application to lower limb exoskeletons,” Robot. Autonomous Syst. 73, 68–77 (2014).Google Scholar
16. Karavas, N., Ajoudani, A., Tsagarakis, N., Saglia, J., Bicchi, A. and Caldwell, D., “Tele-impedance based assistive control for a compliant knee exoskeleton,” Robot. Autonomous Syst. 73, 78–90 (2014).CrossRefGoogle Scholar
17. Taherifar, A., Vossoughi, G. and Selk Ghafari, A., “Optimal target impedance selection of the robot interacting with human,” Adv. Robot. 31 (8), 1–13 (2017).CrossRefGoogle Scholar
18. Slotine, J.-J. E. and Li, W., Applied Nonlinear Control (No. 1). (Prentice-Hall, Englewood Cliffs, NJ, 1991).Google Scholar
19. Huang, A.-C. and Chien, M.-C., Adaptive Control of Robot Manipulators: A Unified Regressor-free Approach. (World Scientific, 2010).Google Scholar
20. Sharifi, M., Behzadipour, S. and Vossoughi, G., “Nonlinear model reference adaptive impedance control for human–robot interactions,” Control Eng. Practice 32, 9–27 (11//2014).Google Scholar
21. Tsuji, T. and Tanaka, Y., “Bio-mimetic impedance control of robotic manipulator for dynamic contact tasks,” Robot. Autonomous Syst. 56 (4), 306–316 (2008).CrossRefGoogle Scholar
22. Kai, C.-Y. and Huang, A.-C., “A regressor-free adaptive controller for robot manipulators without Slotine and Li's modification,” Robotica 31 (07), 1051–1058 (2013).Google Scholar
23. Desai, R. and Geyer, H., “Robust Swing Leg Placement under Large Disturbances,” Proceedings of the Robotics and Biomimetics (ROBIO), 2012 IEEE International Conference on IEEE, (2012) pp. 265–270.Google Scholar
24. Desai, R. and Geyer, H., “Muscle-reflex Control of Robust Swing Leg Placement,” Proceedings of the Robotics and Automation (ICRA), 2013 IEEE International Conference on IEEE, (2013) pp. 2169–2174.Google Scholar
25. Song, S., Desai, R. and Geyer, H., “Integration of an Adaptive Swing Control into a Neuromuscular Human Walking Model,” Proceedings of the Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, (2013) pp. 4915–4918.Google Scholar