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Passive and active rehabilitation control of human upper-limb exoskeleton robot with dynamic uncertainties

Published online by Cambridge University Press:  08 August 2018

Brahim Brahmi ,
Maarouf Saad ,
Cristobal Ochoa Luna ,
Philippe S. Archambault  and
Mohammad H. Rahman
Brahim Brahmi*
Affiliation:
Electrical Engineering Department, École de technologie supérieure, Montreal, Canada. E-mail: [email protected]
Maarouf Saad
Affiliation:
Electrical Engineering Department, École de technologie supérieure, Montreal, Canada. E-mail: [email protected]
Cristobal Ochoa Luna
Affiliation:
School of Physical & Occupational Therapy, McGill University; Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal, Montreal, Canada. E-mails: [email protected], [email protected]
Philippe S. Archambault
Affiliation:
School of Physical & Occupational Therapy, McGill University; Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal, Montreal, Canada. E-mails: [email protected], [email protected]
Mohammad H. Rahman
Affiliation:
Mechanical Engineering Department, University of Wisconsin-Milwaukee, WI, USA. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper investigates the passive and active control strategies to provide a physical assistance and rehabilitation by a 7-DOF exoskeleton robot with nonlinear uncertain dynamics and unknown bounded external disturbances due to the robot user's physiological characteristics. An Integral backstepping controller incorporated with Time Delay Estimation (BITDE) is used, which permits the exoskeleton robot to achieve the desired performance of working under the mentioned uncertainties constraints. Time Delay Estimation (TDE) is employed to estimate the nonlinear uncertain dynamics of the robot and the unknown disturbances. To overcome the limitation of the time delay error inherent of the TDE approach, a recursive algorithm is used to further reduce its effect. The integral action is employed to decrease the impact of the unmodeled dynamics. Besides, the Damped Least Square method is introduced to estimate the desired movement intention of the subject with the objective to provide active rehabilitation. The controller scheme is to ensure that the robot system performs passive and active rehabilitation exercises with a high level of tracking accuracy and robustness, despite the unknown dynamics of the exoskeleton robot and the presence of unknown bounded disturbances. The design, stability, and convergence analysis are formulated and proven based on the Lyapunov–Krasovskii functional theory. Experimental results with healthy subjects, using a virtual environment, show the feasibility, and ease of implementation of the control scheme. Its robustness and flexibility to deal with parameter variations due to the unknown external disturbances are also shown.

Keywords

Backstepping integral controlTime delay estimationPassive and active rehabilitationTime delay error
Type
Articles
Information
Robotica , Volume 36 , Issue 11 , November 2018 , pp. 1757 - 1779
Copyright
Copyright © Cambridge University Press 2018 

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References

1. Sidney, S., Rosamond, W. D., Howard, V. J. and Luepker, R. V., “The ‘heart disease and stroke statistics-2013 update’ and the need for a national cardiovascular surveillance system,” Circulation 127 (1), 21–23 (2013).Google Scholar
2. De Morand, A., Pratique de la Rééducation Neurologique (Elsevier Masson, Paris, 2014).Google Scholar
3. Xie, S., Advanced Robotics for Medical Rehabilitation (Springer, New York, NY, 2016).Google Scholar
4. Brahim, B., Maarouf, S., Luna, C. O., Abdelkrim, B. and Rahman, M., “Adaptive Iterative Observer Based on Integral Backstepping Control for Upper Extremity Exoskelton Robot,” Proceedings of the 8th International Conference on Modelling, Identification and Control (ICMIC), (2016) pp. 886–891.Google Scholar
5. Brahim, B., Rahman, M. H., Saad, M. and Luna, C. O., “Iterative estimator-based nonlinear backstepping control of a robotic exoskeleton world academy of science, engineering and technology,” Int. J. Mech. Aerosp. Ind. Mechatronic Manuf. Eng. 10 (8), 1313–1319 (2016).Google Scholar
6. Rahman, M. H., Rahman, M. J., Cristobal, O., Saad, M., Kenné, J.-P. and Archambault, P. S., “Development of a whole arm wearable robotic exoskeleton for rehabilitation and to assist upper limb movements,” Robotica 33 (1), 19 (2015).Google Scholar
7. Rahman, M. H., Saad, M., Kenné, J.-P. and Archambault, P. S., “Control of an exoskeleton robot arm with sliding mode exponential reaching law,” Int. J. Control, Autom. Syst. 11 (1), 92–104 (2013).Google Scholar
8. Brahim, B., Ochoa-Luna, C., Saad, M., Assad-Uz-Zaman, M., Islam, M. R. and Rahman, M. H., “A New Adaptive Super-Twisting Control for an Exoskeleton Robot with Dynamic Uncertainties,” Proceedings of the 2017 IEEE Great Lakes Biomedical Conference (GLBC) (2017) pp. 1–1.Google Scholar
9. Slotine, J.-J. E. and Li, W., Applied Nonlinear Control (Prentice-Hall Englewood, Cliffs, NJ, 1991).Google Scholar
10. Rigatos, G., Siano, P. and Abbaszadeh, M., “Nonlinear H-infinity control for 4-DOF underactuated overhead cranes,” Trans. Inst. Meas. Control 40 (7), 2364–2377 (2017).Google Scholar
11. Khalil, H. K. and Grizzle, J., Nonlinear Systems (Prentice hall, New Jersey, 1996).Google Scholar
12. Young, K. D., Utkin, V. I. and Ozguner, U., “A control engineer's guide to sliding mode control,” IEEE Trans. Control Syst. Technol. 7 (3), 328–342 (1999).Google Scholar
13. Fridman, L., “The Problem of Chattering: An Averaging Approach,” Variable Structure Systems, Sliding Mode and Nonlinear Control (Springer, 1999) pp. 363–386.Google Scholar
14. Brahmi, B., Saad, M., Rahman, M. H. and Ochoa-Luna, C., “Cartesian trajectory tracking of a 7-DOF exoskeleton robot based on human inverse kinematics,” IEEE Trans. Syst. Man Cybernetics: Syst. PP (99), 1–12 (2017).Google Scholar
15. Zhou, J. and Wen, C., Adaptive Backstepping Control of Uncertain Systems: Nonsmooth Nonlinearities, Interactions or Time-Variations (Springer-Verlag, Berlin Heidelberg, 2008).Google Scholar
16. Chen, W., Ge, S. S., Wu, J. and Gong, M., “Globally stable adaptive backstepping neural network control for uncertain strict-feedback systems with tracking accuracy known a priori,” IEEE Trans. Neural Networks Learning Syst. 26 (9), 1842–1854 (2015).Google Scholar
17. Li, Z., Huang, Z., He, W. and Su, C.-Y., “Adaptive impedance control for an upper limb robotic exoskeleton using biological signals,” IEEE Trans. Ind. Electron. 64 (2), 1664–1674 (2017).Google Scholar
18. Li, Z., Su, C.-Y., Li, G. and Su, H., “Fuzzy approximation-based adaptive backstepping control of an exoskeleton for human upper limbs,” IEEE Trans. Fuzzy Syst. 23 (3), 555–566 (2015).Google Scholar
19. Yoo, B. K. and Ham, W. C., “Adaptive control of robot manipulator using fuzzy compensator,” IEEE Trans. Fuzzy Syst. 8 (2), 186–199 (2000).Google Scholar
20. Youcef-Toumi, K. and Ito, O., “A time delay controller for systems with unknown dynamics,” J. Dyn. Syst. Meas. Control 112 (1), 133–142 (1990).Google Scholar
21. Brahmi, B., Saad, M., Luna, C. O., Archambault, P. and Rahman, M., “Sliding Mode Control of an Exoskeleton Robot Based on Time Delay Estimation,” Proceedings of the International Conference on Virtual Rehabilitation (ICVR) (2017) pp. 1–2.Google Scholar
22. Brahmi, B., Saad, M., Ochoa-Luna, C. and Rahman, M. H., “Adaptive Control of an Exoskeleton Robot with Uncertainties on Kinematics and Dynamics,” Proceedings of the International Conference on Rehabilitation Robotics (ICORR) (2017) pp. 1369–1374.Google Scholar
23. Jin, M., Lee, J. and Ahn, K. K., “Continuous nonsingular terminal sliding-mode control of shape memory alloy actuators using time delay estimation,” IEEE/ASME Trans. Mechatronics 20 (2), 899–909 (2015).Google Scholar
24. Kim, J., Joe, H., Yu, S.-c., Lee, J. S. and Kim, M., “Time-delay controller design for position control of autonomous underwater vehicle under disturbances,” IEEE Trans. Ind. Electron. 63 (2), 1052–1061 (2016).Google Scholar
25. Karafyllis, I., Malisoff, M., Mazenc, F. and Pepe, P., Recent Results on Nonlinear Delay Control Systems (Springer, New York, NY, 2016).Google Scholar
26. Skjetne, R. and Fossen, T. I., “On Integral Control in Backstepping: Analysis of Different Techniques,” Proceedings of the American Control Conference (2004) pp. 1899–1904.Google Scholar
27. Tan, Y., Chang, J., Tan, H. and Hu, J., “Integral Backstepping Control and Experimental Implementation for Motion System,” Proceedings of the 2000 IEEE International Conference on Control Applications (2000). pp. 367–372.Google Scholar
28. Luo, Y., Liu, Q., Che, X. and Li, L., “Damped least-square method based on chaos anti-control for solving forward displacement of general 6-6-type parallel mechanism,” Int. J. Adv. Robotic Syst. 10 (4), 186 (2013).Google Scholar
29. Liu, C., Song, C., Lu, Q., Liu, Y., Feng, X. and Gao, Y., “Impedance inversion based on L1 norm regularization,” J. Appl. Geophys. 120, 7–13 (2015).Google Scholar
30. Gauthier, P.-A., Camier, C., Lebel, F.-A., Pasco, Y., Berry, A., Langlois, J., Verron, C. and Guastavino, C., “Experiments of multichannel least-square methods for sound field reproduction inside aircraft mock-up: Objective evaluations,” J. Sound Vib. 376, 194–216 (2016).Google Scholar
31. Ferrer, S., Ochoa-Luna, C., Rahman, M., Saad, M. and Archambault, P., “HELIOS: The Human Machine Interface for MARSE Robot,” Proceedings of the 6th International Conference on Human System Interaction (HSI), (IEEE, 2013) (2013) pp. 117–122.Google Scholar
32. Weiss, P. L., Tirosh, E. and Fehlings, D., “Role of virtual reality for cerebral palsy management,” J. Child Neurol. 29 (8), 1119–1124 (2014).Google Scholar
33. Luna, C. O., Rahman, M. H., Saad, M., Archambault, P. and Zhu, W.-H., “Virtual decomposition control of an exoskeleton robot arm,” Robotica 34 (07), 1587–1609 (2016).Google Scholar
34. Rahman, M. H., Kittel-Ouimet, T., Saad, M., Kenné, J.-P. and Archambault, P. S., “Dynamic modeling and evaluation of a robotic exoskeleton for upper-limb rehabilitation,” Int. J. Inform. Acquisition 8 (01), 83–102 (2011).Google Scholar
35. Craig, J. J., Introduction to Robotics: Mechanics and Control (Pearson Prentice Hall Upper Saddle River, 2005).Google Scholar
36. Siciliano, B., Sciavicco, L., Villani, L. and Oriolo, G., Kinematics (Springer, 2009).Google Scholar
37. Spong, M. W., Hutchinson, S. and Vidyasagar, M., Robot Modeling and Control (Wiley, New York, 2006).Google Scholar
38. Ochoa Luna, C., Rahman, M. Habibur, Saad, M., Archambault, P. S. and Ferrer, S. Bruce, “Admittance-based upper limb robotic active and active-assistive movements,” Int. J. Adv. Robot. Syst. 12 (9), 117 (2015).Google Scholar
39. Khan, A. M., Yun, D.-w., Ali, M. A., Zuhaib, K. M., Yuan, C., Iqbal, J., Han, J., Shin, K. and Han, C., “Passivity based adaptive control for upper extremity assist exoskeleton,” Int. J. Control Autom. Syst. 14 (1), 291–300 (2016).Google Scholar
40. Lawson, C. L. and Hanson, R. J., Solving Least Squares Problems (SIAM, Philadelphia, PA, 1995).Google Scholar
41. Wampler, C. W., “Manipulator inverse kinematic solutions based on vector formulations and damped least-squares methods,” IEEE Trans. Syst. Man Cybern. 16 (1), 93–101 (1986).Google Scholar
42. Nakamura, Y. and Hanafusa, H., “Inverse kinematic solutions with singularity robustness for robot manipulator control,” ASME, Trans. J. Dyn. Syst. Meas. Control 108, 163–171 (1986).Google Scholar
43. Kali, Y., Saad, M., Benjelloun, K. and Benbrahim, M., “Control of Uncertain Robot Manipulators Using Integral Backstepping and Time Delay Estimation,” Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics (ICINCO) (2016) pp. 145–151.Google Scholar