Iterative Estimator-Based Nonlinear Backstepping Control of a Robotic Exoskeleton
A repetitive training movement is an efficient method
to improve the ability and movement performance of stroke survivors
and help them to recover their lost motor function and acquire new
skills. The ETS-MARSE is seven degrees of freedom (DOF)
exoskeleton robot developed to be worn on the lateral side of the
right upper-extremity to assist and rehabilitate the patients with
upper-extremity dysfunction resulting from stroke. Practically,
rehabilitation activities are repetitive tasks, which make the
assistive/robotic systems to suffer from repetitive/periodic
uncertainties and external perturbations induced by the high-order
dynamic model (seven DOF) and interaction with human muscle
which impact on the tracking performance and even on the stability
of the exoskeleton. To ensure the robustness and the stability of the
robot, a new nonlinear backstepping control was implemented with
designed tests performed by healthy subjects. In order to limit and to
reject the periodic/repetitive disturbances, an iterative estimator was
integrated into the control of the system. The estimator does not need
the precise dynamic model of the exoskeleton. Experimental results
confirm the robustness and accuracy of the controller performance to
deal with the external perturbation, and the effectiveness of the
iterative estimator to reject the repetitive/periodic disturbances.
Malouin, F., et al., Nouvelles perspectives en réadaptation motrice après un accident vasculaire cérébral. M/S: médecine sciences, 2003. 19(10): p. 994-998.
[2] Sidney, S., et al., The “heart disease and stroke statistics—2013 update” and the need for a national cardiovascular surveillance system. Circulation, 2013. 127(1): p. 21-23.
[3] De Morand, A., Pratique de la rééducation neurologique. 2014: Elsevier Masson.
[4] Fasoli, S.E., et al., Does shorter rehabilitation limit potential recovery poststroke? Neurorehabilitation and neural repair, 2004. 18(2): p. 88-94.
[5] Reinkensmeyer, D.J., et al., Understanding and treating arm movement impairment after chronic brain injury: progress with the ARM guide. Journal of rehabilitation research and development, 2000. 37(6): p. 653.
[6] Culmer, P.R., et al., A control strategy for upper limb robotic rehabilitation with a dual robot system. Mechatronics, IEEE/ASME Transactions on, 2010. 15(4): p. 575-585.
[7] Nef, T., et al. ARMin-Exoskeleton for arm therapy in stroke patients. in Rehabilitation Robotics, 2007. ICORR 2007. IEEE 10th International Conference on. 2007. IEEE.
[8] Rahman, M.H., et al., Control of an exoskeleton robot arm with sliding mode exponential reaching law. International Journal of Control, Automation and Systems, 2013. 11(1): p. 92-104.
[9] Rahman, M.H., et al., Development of a whole arm wearable robotic exoskeleton for rehabilitation and to assist upper limb movements. Robotica, 2015. 33(01): p. 19-39.
[10] Yu, W. and J. Rosen. A novel linear PID controller for an upper limb exoskeleton. in Decision and Control (CDC), 2010 49th IEEE Conference on. 2010. IEEE.
[11] Yu, W. and J. Rosen, Neural PID control of robot manipulators with application to an upper limb exoskeleton. Cybernetics, IEEE Transactions on, 2013. 43(2): p. 673-684.
[12] Rahman, M.H., et al. Tele-operation of a robotic exoskeleton for rehabilitation and passive arm movement assistance. in Robotics and Biomimetics (ROBIO), 2011 IEEE International Conference on. 2011. IEEE.
[13] Rahman, M.H., et al., Nonlinear sliding mode control implementation of an upper limb exoskeleton robot to provide passive rehabilitation therapy, in Intelligent Robotics and Applications. 2012, Springer. p. 52-62.
[14] Sun, F.-C., Z.-Q. Sun, and G. Feng, An adaptive fuzzy controller based on sliding mode for robot manipulators. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 1999. 29(5): p. 661-667.
[15] Ciliz, M.K., Adaptive control of robot manipulators with neural network based compensation of frictional uncertainties. Robotica, 2005. 23(02): p. 159-167.
[16] Liu, Y.-J., S. Tong, and C.P. Chen, Adaptive fuzzy control via observer design for uncertain nonlinear systems with unmodeled dynamics. Fuzzy Systems, IEEE Transactions on, 2013. 21(2): p. 275-288.
[17] Li, Z., et al., Fuzzy approximation-based adaptive backstepping control of an exoskeleton for human upper limbs. Fuzzy Systems, IEEE Transactions on, 2015. 23(3): p. 555-566.
[18] Khalil, H.K. and J. Grizzle, Nonlinear systems. Vol. 3. 1996: Prentice hall New Jersey.
[19] Benaskeur, A.R., Aspects de l'application du backstepping adaptatif à la commande décentralisée des systèmes non linéaires. 2002, Université Laval.
[20] Jin, X. and J.-X. Xu, Iterative learning control for output-constrained systems with both parametric and nonparametric uncertainties. Automatica, 2013. 49(8): p. 2508-2516.
[21] Xu, J.-X. and R. Yan, Synchronization of chaotic systems via learning control. International journal of bifurcation and Chaos, 2005. 15(12): p. 4035-4041.
[22] Ming-Xuan, S. and Y. Qiu-Zhen, Error tracking of iterative learning control systems. Acta Automatica Sinica, 2013. 39(3): p. 251-262.
[23] Craig, J.J., Introduction to robotics: mechanics and control. Vol. 3. 2005: Pearson Prentice Hall Upper Saddle River.
[24] Ding, Z., Asymptotic rejection of general periodic disturbances in output-feedback nonlinear systems. Automatic Control, IEEE Transactions on, 2006. 51(2): p. 303-308.
[25] Ding, Z., Asymptotic rejection of a class of periodic disturbances in nonlinear output-feedback systems. Control Theory & Applications, IET, 2007. 1(3): p. 699-703.
[26] Hurmusiadis, V., S. Barrick, and C. Briscoe. Interactive functional anatomy. in ACM SIGGRAPH 2003 Sketches & Applications. 2003. ACM.
[27] Slotine, J.-J.E. and W. Li, Applied nonlinear control. Vol. 199. 1991: Prentice-Hall Englewood Cliffs, NJ.
Malouin, F., et al., Nouvelles perspectives en réadaptation motrice après un accident vasculaire cérébral. M/S: médecine sciences, 2003. 19(10): p. 994-998.
[2] Sidney, S., et al., The “heart disease and stroke statistics—2013 update” and the need for a national cardiovascular surveillance system. Circulation, 2013. 127(1): p. 21-23.
[3] De Morand, A., Pratique de la rééducation neurologique. 2014: Elsevier Masson.
[4] Fasoli, S.E., et al., Does shorter rehabilitation limit potential recovery poststroke? Neurorehabilitation and neural repair, 2004. 18(2): p. 88-94.
[5] Reinkensmeyer, D.J., et al., Understanding and treating arm movement impairment after chronic brain injury: progress with the ARM guide. Journal of rehabilitation research and development, 2000. 37(6): p. 653.
[6] Culmer, P.R., et al., A control strategy for upper limb robotic rehabilitation with a dual robot system. Mechatronics, IEEE/ASME Transactions on, 2010. 15(4): p. 575-585.
[7] Nef, T., et al. ARMin-Exoskeleton for arm therapy in stroke patients. in Rehabilitation Robotics, 2007. ICORR 2007. IEEE 10th International Conference on. 2007. IEEE.
[8] Rahman, M.H., et al., Control of an exoskeleton robot arm with sliding mode exponential reaching law. International Journal of Control, Automation and Systems, 2013. 11(1): p. 92-104.
[9] Rahman, M.H., et al., Development of a whole arm wearable robotic exoskeleton for rehabilitation and to assist upper limb movements. Robotica, 2015. 33(01): p. 19-39.
[10] Yu, W. and J. Rosen. A novel linear PID controller for an upper limb exoskeleton. in Decision and Control (CDC), 2010 49th IEEE Conference on. 2010. IEEE.
[11] Yu, W. and J. Rosen, Neural PID control of robot manipulators with application to an upper limb exoskeleton. Cybernetics, IEEE Transactions on, 2013. 43(2): p. 673-684.
[12] Rahman, M.H., et al. Tele-operation of a robotic exoskeleton for rehabilitation and passive arm movement assistance. in Robotics and Biomimetics (ROBIO), 2011 IEEE International Conference on. 2011. IEEE.
[13] Rahman, M.H., et al., Nonlinear sliding mode control implementation of an upper limb exoskeleton robot to provide passive rehabilitation therapy, in Intelligent Robotics and Applications. 2012, Springer. p. 52-62.
[14] Sun, F.-C., Z.-Q. Sun, and G. Feng, An adaptive fuzzy controller based on sliding mode for robot manipulators. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 1999. 29(5): p. 661-667.
[15] Ciliz, M.K., Adaptive control of robot manipulators with neural network based compensation of frictional uncertainties. Robotica, 2005. 23(02): p. 159-167.
[16] Liu, Y.-J., S. Tong, and C.P. Chen, Adaptive fuzzy control via observer design for uncertain nonlinear systems with unmodeled dynamics. Fuzzy Systems, IEEE Transactions on, 2013. 21(2): p. 275-288.
[17] Li, Z., et al., Fuzzy approximation-based adaptive backstepping control of an exoskeleton for human upper limbs. Fuzzy Systems, IEEE Transactions on, 2015. 23(3): p. 555-566.
[18] Khalil, H.K. and J. Grizzle, Nonlinear systems. Vol. 3. 1996: Prentice hall New Jersey.
[19] Benaskeur, A.R., Aspects de l'application du backstepping adaptatif à la commande décentralisée des systèmes non linéaires. 2002, Université Laval.
[20] Jin, X. and J.-X. Xu, Iterative learning control for output-constrained systems with both parametric and nonparametric uncertainties. Automatica, 2013. 49(8): p. 2508-2516.
[21] Xu, J.-X. and R. Yan, Synchronization of chaotic systems via learning control. International journal of bifurcation and Chaos, 2005. 15(12): p. 4035-4041.
[22] Ming-Xuan, S. and Y. Qiu-Zhen, Error tracking of iterative learning control systems. Acta Automatica Sinica, 2013. 39(3): p. 251-262.
[23] Craig, J.J., Introduction to robotics: mechanics and control. Vol. 3. 2005: Pearson Prentice Hall Upper Saddle River.
[24] Ding, Z., Asymptotic rejection of general periodic disturbances in output-feedback nonlinear systems. Automatic Control, IEEE Transactions on, 2006. 51(2): p. 303-308.
[25] Ding, Z., Asymptotic rejection of a class of periodic disturbances in nonlinear output-feedback systems. Control Theory & Applications, IET, 2007. 1(3): p. 699-703.
[26] Hurmusiadis, V., S. Barrick, and C. Briscoe. Interactive functional anatomy. in ACM SIGGRAPH 2003 Sketches & Applications. 2003. ACM.
[27] Slotine, J.-J.E. and W. Li, Applied nonlinear control. Vol. 199. 1991: Prentice-Hall Englewood Cliffs, NJ.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:73446", author = "Brahmi Brahim and Mohammad Habibur Rahman and Maarouf Saad and Cristóbal Ochoa Luna", title = "Iterative Estimator-Based Nonlinear Backstepping Control of a Robotic Exoskeleton", abstract = "A repetitive training movement is an efficient method
to improve the ability and movement performance of stroke survivors
and help them to recover their lost motor function and acquire new
skills. The ETS-MARSE is seven degrees of freedom (DOF)
exoskeleton robot developed to be worn on the lateral side of the
right upper-extremity to assist and rehabilitate the patients with
upper-extremity dysfunction resulting from stroke. Practically,
rehabilitation activities are repetitive tasks, which make the
assistive/robotic systems to suffer from repetitive/periodic
uncertainties and external perturbations induced by the high-order
dynamic model (seven DOF) and interaction with human muscle
which impact on the tracking performance and even on the stability
of the exoskeleton. To ensure the robustness and the stability of the
robot, a new nonlinear backstepping control was implemented with
designed tests performed by healthy subjects. In order to limit and to
reject the periodic/repetitive disturbances, an iterative estimator was
integrated into the control of the system. The estimator does not need
the precise dynamic model of the exoskeleton. Experimental results
confirm the robustness and accuracy of the controller performance to
deal with the external perturbation, and the effectiveness of the
iterative estimator to reject the repetitive/periodic disturbances.", keywords = "Backstepping control, iterative control,
rehabilitation, ETS-MARSE.", volume = "10", number = "8", pages = "1382-7", }