Abstract
In this paper, to address motor dysfunction caused by factors such as stroke or traffic accidents, a kind of upper limb rehabilitation robot is designed for rehabilitation training. The rehabilitation robot is driven by series elastic actuator (SEA) to make the upper limb rehabilitation robot have flexible output. Flexible output can improve the compliance and safety between the patient and the rehabilitation robot, but impedance control method is needed to ensure the compliance of human–robot interaction. In order to solve the human–robot interaction problem of SEA–based upper limb rehabilitation robot, the dynamic model and an impedance control are established for the SEA–based upper limb rehabilitation robot. The impedance control method of upper limb rehabilitation robot based on terminal position is designed in detail. Aiming at the designed impedance control method, a numerical simulation model is established for the upper limb rehabilitation robot, and the accuracy of the model is verified by the simulation of the upper limb rehabilitation robot. The numerical results show that the impedance controller can meet the needs of the rehabilitation training of the upper limb rehabilitation robot, which improves the coordination of human–robot interaction in the rehabilitation process.
The work is supported in part by the National Natural Science Foundation of China under grants 62173048, 61873304 and in part by the China Postdoctoral Science Foundation Funded Project under grants 2018M641784 and 2019T120240, and also in part by the Changchun Science and Technology Project under grant 21ZY41.
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References
Bertani, R., Melegari, C., De Cola, M.C., Bramanti, A., Bramanti, P., Calabrò, R.S.: Effects of robot-assisted upper limb rehabilitation in stroke patients: a systematic review with meta-analysis. Neurol. Sci. 38(9), 1561–1569 (2017). https://doi.org/10.1007/s10072-017-2995-5
Sun, Z.B., Liu, Y., Wang, G., et al.: Discrete-time noise-tolerant Z-type model for online solving nonlinear time-varying equations in the presence of noises. J. Comput. Appl. Math. 403(15), 113824 (2022)
Akdo, A.E., Aktan, M.E., Koru, A.T., et al.: Hybrid impedance control of a robot manipulator for wrist and forearm rehabilitation: performance analysis and clinical results. Mechatronics 49, 77–91 (2017)
Brahmi, B., Driscoll, M., El Bojairami, I.K., Saad, M., Brahmi, A.: Novel adaptive impedance control for exoskeleton robot for rehabilitation using a nonlinear time-delay disturbance observer. ISA Trans. 108, 381–392 (2020)
Wu, Q., Wang, X., Bai, C., et al.: Development of an RBFN-based neural-fuzzy adaptive control strategy for an upper limb rehabilitation exoskeleton. Mechatronics 53, 85–94 (2018)
Jalaeian, F.M., Fateh, M.M., Rahimiyan, M.: Optimal predictive impedance control in the presence of uncertainty for a lower limb rehabilitation robot. J. Syst. Sci. Complexity 33, 310–1329 (2020)
Mancisidor, A., Zubizarretaa, A., Cabanes, I., et al.: Kinematical and dynamical modeling of a multipurpose upper limbs rehabilitation robot. Robot. Comput. Integr. Manuf. 49(7), 374–387 (2018)
Yang, T., Gao, X., Dai, F.: New hybrid AD methodology for minimizing the total amount of information content: a case study of rehabilitation robot design. Chin. J. Mech. Eng. 33(1), 51–60 (2020)
Sun, Z., Zhao, L., Liu, K., Jin, L., Yu, J., Li, C.: An advanced form-finding of tensegrity structures aided with noise-tolerant zeroing neural network. Neural Comput. Appl. 34(8), 6053–6066 (2021). https://doi.org/10.1007/s00521-021-06745-6
Sun, Z.B., Wang, G., Jin, L., et al.: Noise-suppressing zeroing neural network for online solving time-varying matrix square roots problems: a control-theoretic approach. Expert Syst. Appl. 192(15), 116272 (2022)
Ji, L.: Quantitative assessment of motor function by an end-effector upper limb rehabilitation robot based on admittance control. Appl. Sci. 11, 112–132 (2021)
Meng, Q., Jiao, Z., Yu, H., et al.: Design and evaluation of a novel upper limb rehabilitation robot with space training based on an end effector. Mech. Sci. 1, 639–648 (2021)
Madani, M., Moallem, M.: Hybrid position/force control of a flexible parallel manipulator. J. Franklin Inst. 348(6), 999–1012 (2011)
Wang, J., Liu, J., Zhang, G., et al.: Periodic event-triggered sliding mode control for lower limb exoskeleton based on human-robot cooperation. ISA Trans. 123, 87–97 (2022)
Chai, Y.Y., Liu, K.P., Li, C.X., et al.: A novel method based on long short term memory network and discrete-time zeroing neural algorithm for upper-limb continuous estimation using sEMG signals. Biomed. Signal Process. Control 67, 1746–8094 (2021)
Krebs, H.I., Volpe, B.T., Aisen, M.L., et al.: Increasing productivity and quality of care: robot-aided neuro-rehabilitation. J. Rehabil. Res. Dev. 37(6), 639–652 (2000)
Zhang, Q., Sun, D., Qian, W., et al.: Modeling and control of a cable-driven rotary series elastic actuator for an upper limb rehabilitation robot. Front. Neurorobot. 14, 13 (2020)
Chen, T., Casas, R., Lum, P.S.: An elbow exoskeleton for upper limb rehabilitation with series elastic actuator and cable-driven differential. IEEE Trans. Robot. 35(6), 1464–1474 (2019)
Banala, S.K., Kim, S.H., Agrawal, S.K., et al.: Robot assisted gait training with active leg exoskeleton (ALEX). IEEE Trans. Neural Syst. Rehabil. Eng. 17(1), 2–8 (2009)
Hu, J., Hou, Z., Zhang, F., et al.: Training strategies for a lower limb rehabilitation robot based on impedance control. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6032–6035 (2012)
Riener, R., Lunenburger, L., Jezernik, S., et al.: Patient-cooperative strategies for robot-aided treadmill training: first experimental results. IEEE Trans. Neural Syst. Rehabil. Eng. 13(3), 380–394 (2055)
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Gu, J., Xu, C., Liu, K., Zhao, L., He, T., Sun, Z. (2022). Impedance Control of Upper Limb Rehabilitation Robot Based on Series Elastic Actuator. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13457. Springer, Cham. https://doi.org/10.1007/978-3-031-13835-5_13
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