Abstract
Robot-assisted rehabilitation following neurological injury is most successful when subject participation is maximized in the training tasks. Developing control strategies that can provide subject-specific assistance is accordingly an active area of research. For robot-assisted rehabilitation training, it is challenging to adapt the robotic assistance to each patient’s impairment, and model-based control methods in previous studies are difficult to implement because of the computational complexity of human-robot interaction dynamics and changes of human active efforts during rehabilitation exercises. This study implements adaptive modeling and control for an two-DOF upper-limb rehabilitation robot by combining an RBF-based feedforward controller with a feedback impedance controller. Simulation and experiment results show that, the RBF neural network is able to adaptively establish the human-robot dynamics as well as estimating the human efforts, and the impedance controller guarantees compliant human-robot interaction and regulates the maximal tolerated tracking error. Besides, the proposed controller is defined in the robot workspace, thus is easy to be generalized to be used for multi-DOFs exoskeleton-type rehabilitation robots.
This research is supported by in part by National Natural Science Foundation of China (Grants #61603386, U1613228, 61720106012, 61533016, 61421004) and Beijing Natural Science Foundation (Grant L172050, Z161100001516004).
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References
Riener, R., Nef, T., Colombo, G.: Robot-aided neurorehabilitation of the upper extremities. Med. Biol. Eng. Comput. 43(1), 2–10 (2005)
Lo, A.C., Guarino, P.D., Richards, L.G., Haselkorn, J.K.: Robot-assisted therapy for long-term upper-limb impairment after stroke. N. Engl. J. Med. 362(19), 1772–1783 (2010)
Maciejasz, P., Eschweiler, J., Gerlach-Hahn, K., Jansen-Troy, A., Leonhardt, S.: A survey on robotic devices for upper limb rehabilitation. J. Neuroeng. Rehabil. 11(1), 3 (2014)
Marchal-Crespo, L., Reinkensmeyer, D.J.: Review of control strategies for robotic movement training after neurologic injury. J. Neuroeng. Rehabil. 6(1), 20 (2009)
Hogan, N., Krebs, H.I., Rohrer, B., Palazzolo, J.J.: Motions or muscles? Some behavioral factors underlying robotic assistance of motor recovery. J. Rehabil. Res. Dev. 43(5), 605 (2006)
Prange, G.B., Jannink, M.J., Groothuis-Oudshoorn, C.G., Hermens, H.J., IJzerman, M.J.: Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke. J. Rehabil. Res. Dev. 43(2), 171 (2006)
Reinkensmeyer, D.J., Wolbrecht, E., Bobrow, J.: A computational model of human-robot load sharing during robot-assisted arm movement training after stroke. In: Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4019–4023 (2007)
Lotte, F., et al.: A review of classification algorithms for eeg-based brain–computer interfaces: a 10 year update. J. Neural Eng. 15(3), 031005 (2018)
Nazmi, N., Abdul Rahman, M.A., Yamamoto, S.-I., Ahmad, S.A., Zamzuri, H. Mazlan, S.A.: A review of classification techniques of EMG signals during isotonic and isometric contractions. Sensors 6(8), 1304 (2016)
Sartori, M., Reggiani, M., Farina, D., Lloyd, D.G.: EMG-driven forward-dynamic estimation of muscle force and joint moment about multiple degrees of freedom in the human lower extremity. PloS one 7(12), e52618 (2012)
Wang, W., et al.: Toward patients motion intention recognition: dynamics modeling and identification of ilegan LLRR under motion constraints. IEEE Trans. Syst. Man Cybern. Syst. 46(7), 980–992 (2016)
Peng, L., Hou, Z.G., Peng, L., Wang, W.: Design of CASIA-ARM: a novel rehabilitation robot for upper limbs. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5611–5616 (2015)
Peng, L., Hou, Z.G., Peng, L., Luo, L., Wang, W.: Robot assisted rehabilitation of the arm after stroke: prototype design and clinical evaluation. Sci. China Inf. Sci. 60(7), 073201 (2017)
Slotine, J.J.E., Li, W.: Applied Nonlinear Control. Prentice hall, Englewood Cliffs (1991)
Winter, D.A.: Biomechanics and Motor Control of Human Movement. Wiley, New York (2009)
Peng, L., Hou, Z.G., Wang, W.: Dynamic modeling and control of a parallel upper-limb rehabilitation robot. In: Proceedings of IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 532–537 (2015)
Hogan, N.: An organizing principle for a class of voluntary movements. J. Neurosci. 4(11), 2745–2754 (1984)
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Peng, L., Wang, C., Luo, L., Chen, S., Hou, ZG., Wang, W. (2018). Adaptive Modeling and Control of an Upper-Limb Rehabilitation Robot Using RBF Neural Networks. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11307. Springer, Cham. https://doi.org/10.1007/978-3-030-04239-4_21
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