Disturbance Observer-Based Patient-Cooperative Control of a Lower Extremity Rehabilitation Exoskeleton
Tóm tắt
Many patients with stroke are suffering lower limb locomotor dysfunctions all over the world. Body weight supported treadmill training has proven to be an effective post-stroke rehabilitation training method for these people’s recovery. Nowadays, lower extremity rehabilitation exoskeleton composed of a pair of mechanical legs has been introduced into body weight supported treadmill training, which can guide and assist the movements of the patient’s legs. However, active movements of the patient are hardly to be achieved when the rehabilitation exoskeleton is controlled by a commonly utilized position-based passive strategy. Considering the restriction above, a weight supported rehabilitation training exoskeleton device was designed in this paper to ensure the stroke patient can participate in rehabilitation training voluntarily. To realize this goal, a patient-cooperative rehabilitation training strategy based on adaptive impedance control is adopted for the swing phase in the training. Human–exoskeleton interaction torques are evaluated by a backpropagation neural network and a disturbance observer whose stability is proved by Lyapunov’s law. With no additional demand of interaction torque sensors, the complexity of this system is simplified and the cost is reduced. In order to promote the involvement of patient during the rehabilitation training, fuzzy algorithm is used to adjust the impedance parameters according to the human–exoskeleton interaction torques. The effectiveness of the whole rehabilitation control strategy is demonstrated by experimental results.
Tài liệu tham khảo
Wiersma, A. M. (2017). Augmenting plasticity and recovery from stroke by modulating the extracellular matrix of the central nervous system. Edmonton: University of Alberta.
Takeuchi, N., Izumi, S. I., Ota, J., & Ueda, J. (2016). Neural plasticity on body representations: Advancing translational rehabilitation. Neural Plasticity,2016, 9737569. https://doi.org/10.1155/2016/9737569.
Turolla, A., Venneri, A., Farina, D., Cagnin, A., & Cheung, V. C. K. (2018). Rehabilitation induced neural plasticity after acquired brain injury. Neural Plasticity,2018, 6565418. https://doi.org/10.1155/2018/6565418.
Dahlin, L. B., Andersson, G., Backman, C., Svensson, H., & Bjorkman, A. (2017). Rehabilitation, using guided cerebral plasticity, of a brachial plexus injury treated with intercostal and phrenic nerve transfers. Frontiers in Neurology,8, 72. https://doi.org/10.3389/fneur.2017.00072.
Gama, G. L., Celestino, M. L., Barela, J. A., Forrester, L., Whitall, J., & Barela, A. M. (2017). Effects of gait training with body weight support on a treadmill versus overground in individuals with stroke. Archives of Physical Medicine and Rehabilitation,98(4), 738–745. https://doi.org/10.1016/j.apmr.2016.11.022.
Mehrholz, J., Thomas, S., & Elsner, B. (2017). Treadmill training and body weight support for walking after stroke. Cochrane Database of Systematic Reviews,8(8), CD002840. https://doi.org/10.1002/14651858.CD002840.pub4.
Meng, W., Liu, Q., Zhou, Z. D., Ai, Q. S., Sheng, B., & Xie, S. Q. (2015). Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation. Mechatronics,31, 132–145. https://doi.org/10.1016/j.mechatronics.2015.04.005.
Colombo, G., Jörg, M., & Jezernik, S. (2002). Automatisiertes Lokomotionstraining auf dem Laufband (Automated Locomotor Training on the Treadmill). at - Automatisierungstechnik Methoden und Anwendungen der Steuerungs-, Regelungs- und Informationstechnik,50, 287.
Zhang, L., Chen, W., Wang, J., & Zhang, J. (2018). Adaptive robust slide mode trajectory tracking controller for lower extremity rehabilitation exoskeleton. In Proceedings of the 2018 13th IEEE conference on industrial electronics and applications (pp. 992–997).
Zakaria, M. A., Majeed, A. P. P. A., Taha, Z., Alim, M. M., & Baarath, K. (2018). Forward and inverse predictive model for the trajectory tracking control of a lower limb exoskeleton for gait rehabilitation: Simulation modelling analysis. In 4th Asia Pacific conference on manufacturing systems and the 3rd international manufacturing engineering conference. IOP conference series-materials science and engineering (Vol. 319, p. 012052).
Quy-Thinh, D., & Yamamoto, S.-I. (2018). Assist-as-needed control of a robotic orthosis actuated by pneumatic artificial muscle for gait rehabilitation. Applied Sciences-Basel,8(4), 499. https://doi.org/10.3390/app8040499.
Li, Z., Dong, W., Wang, L., Chen, C., Wang, J., Du, Z., et al. (2018). Lower limb exoskeleton hybrid phase control based on fuzzy gain sliding mode controller. In 2018 2nd international conference on robotics and automation sciences.
Huang, R., Peng, Z., Cheng, H., Hu, J., Qiu, J., Zou, C., et al. (2018). Learning-based walking assistance control strategy for a lower limb exoskeleton with hemiplegia patients. In 2018 IEEE international conference on intelligent robots and systems (pp. 2280–2285).
Han, S., Wang, H., Tian, Y., & IEEE (2018). Adaptive computed torque control based on RBF network for a lower limb exoskeleton. In 2018 IEEE 15th international workshop on advanced motion control (pp. 35–40).
Long, Y., Du, Z., Cong, L., Wang, W., Zhang, Z., & Dong, W. (2017). Active disturbance rejection control based human gait tracking for lower extremity rehabilitation exoskeleton. ISA Transactions,67, 389–397. https://doi.org/10.1016/j.isatra.2017.01.006.
Taherifar, A., Vossoughi, G., & Ghafari, A. S. (2018). Assistive-compliant control of wearable robots for partially disabled individuals. Control Engineering Practice,74, 177–190. https://doi.org/10.1016/j.conengprac.2018.02.004.
Taherifar, A., Vossoughi, G., & Ghafari, A. S. (2018). Variable admittance control of the exoskeleton for gait rehabilitation based on a novel strength metric. Robotica,36(3), 427–447. https://doi.org/10.1017/s0263574717000480.
Luo, R., Sun, S., Zhao, X., Zhang, Y., & Tang, Y. (2018). Adaptive CPG-based impedance control for assistive lower limb exoskeleton. In 2018 IEEE international conference on robotics and biomimetics.
Huang, G., Zhang, W., Meng, F., Yu, Z., Chen, X., Ceccarelli, M., et al. (2018). Master–slave control of an intention-actuated exoskeletal robot for locomotion and lower extremity rehabilitation. International Journal of Precision Engineering and Manufacturing,19(7), 983–991. https://doi.org/10.1007/s12541-018-0116-x.
Chen, G., Ye, J., Liu, Q., Duan, L., Li, W., Wu, Z., et al. (2018). Adaptive control strategy for gait rehabilitation robot to assist-when-needed. In Proceedings of 2018 IEEE international conference on real-time computing and robotics.
Vallery, H., Duschau-Wicke, A., & Riener, R. (2009). Generalized elasticities improve patient-cooperative control of rehabilitation robots. In 2009 IEEE international conference on rehabilitation robotics, IEEE (pp. 535–541). https://doi.org/10.1109/icorr.2009.5209595.
He, Y., Eguren, D., Azorin, J. M., Grossman, R. G., Trieu Phat, L., & Contreras-Vidal, J. L. (2018). Brain–machine interfaces for controlling lower-limb powered robotic systems. Journal of Neural Engineering,15(2), 021004. https://doi.org/10.1088/1741-2552/aaa8c0.
Villa-Parra, A. C., Delisle-Rodriguez, D., Botelho, T., Mayor, J. J. V., Delis, A. L., Carelli, R., et al. (2018). Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG. Research on Biomedical Engineering,34(3), 198–210. https://doi.org/10.1590/2446-4740.07417.
Sherwani, K., Kumar, N., & Khan, M. (2018). Effect of voluntary and involuntary joint movement on EEG signals. Journal of Scientific and Industrial Research,77(12), 710–712.
Ubeda, A., Azorin, J. M., Farina, D., & Sartori, M. (2018). Estimation of neuromuscular primitives from EEG slow cortical potentials in incomplete spinal cord injury individuals for a new class of brain–machine interfaces. Frontiers in Computational Neuroscience. https://doi.org/10.3389/fncom.2018.00003.
Sacco, K., Belforte, G., Eula, G., Raparelli, T., Sirolli, S., Geda, E., et al. (2018). PIGRO: An active exoskeleton for robotic neurorehabilitation training driven by an electro-pneumatic control. In Advances in service and industrial robotics. Mechanisms and machine science (Vol. 49, pp. 845–853).
Hecht-Nielsen, R. (1989). Theory of the backpropagation neural network. In International joint conference on neural networks.
Hogan, N. (1984) Impedance control: An approach to manipulation. In American control conference.
Riener, R., Lunenburger, L., Jezernik, S., Anderschitz, M., Colombo, G., & Dietz, V. (2005). Patient-cooperative strategies for robot-aided treadmill training: First experimental results. IEEE Transactions on Neural Systems and Rehabilitation Engineering,13(3), 380–394. https://doi.org/10.1109/TNSRE.2005.848628.
Le Chau, N., Dao, T.-P., & Dang, V. A. (2019). An efficient hybrid approach of improved adaptive neural fuzzy inference system and teaching learning-based optimization for design optimization of a jet pump-based thermoacoustic-Stirling heat engine. Neural Computing Applications. https://doi.org/10.1007/s00521-019-04249-y.
Dao, T.-P. (2016). Multiresponse optimization of a compliant guiding mechanism using hybrid Taguchi-grey based fuzzy logic approach. Mathematical Problems in Engineering. https://doi.org/10.1155/2016/5386893.