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Internal Model Control and Experimental Study of Ankle Rehabilitation Robot

Published online by Cambridge University Press:  29 July 2020

Lan Wang ,
Ying Chang Open the ORCID record for Ying Chang [Opens in a new window]  and
Haitao Zhu
Lan Wang
Affiliation:
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang Province, PRChina E-mails: [email protected], [email protected]
Ying Chang*
Affiliation:
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang Province, PRChina E-mails: [email protected], [email protected]
Haitao Zhu
Affiliation:
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang Province, PRChina E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

In the present work, the ankle rehabilitation robot (ARR) dynamic model that implements a new series of connection control strategies is introduced. The dynamic models are presented in this regard. This model analyzes the robot LuGre friction model and the nonlinear disturbance model. To improve the ARR system’s rapidity and robustness, a composite 2-degree of freedom (2-DOF) internal model control (IMC) controller is presented. The control performance of the compound 2-DOF IMC controller is simulated and analyzed in the present work. The simulation shows that the composite 2-DOF IMC controller has high following performance. For practical testing purposes, 1-DOF passive training and predetermined trajectory following have been completed for different swing amplitudes and frequencies. Moreover, the thrust and tension torque of the robotic dynamic and static loading characteristics are studied in active control mode. The experimental results show the effectiveness of passive training of the given trajectory and impedance training active control strategy. This paper gives the specific functions of ARR.

Keywords

Ankle rehabilitation robotIMC controlImpedance trainingNonlinearityDynamic model
Type
Articles
Information
Robotica , Volume 38 , Issue 5 , May 2020 , pp. 940 - 956
Copyright
© Cambridge University Press 2019

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References

Sawicki, G. S. and Ferris, D. P., “A pneumatically powered knee-ankle-foot orthosis (KAFO) with myoelectric activation and inhibition,” J. NeuroEng. Rehabil. 6(1), 1–16 (2009).CrossRefGoogle Scholar
Lu, R., Li, Z., Su, C. and Xue, A., “Development and learning control of a human limb with a rehabilitation exoskeleton,” IEEE Trans. Ind. Electron. 61(7), 3776–3785 (2014).CrossRefGoogle Scholar
Alexander, D. W., Zitzewitz, J. V., Caprez, A., Lunenburger, L. and Riener, R., “Path control: A method for patient-cooperative robot aided gait rehabilitation,” IEEE Trans. Neural Syst. Rehabil. Eng. 18(1), 38–48 (2010).Google Scholar
Deutsch, J., Latonio, J., Burdea, G. and Boian, R., “Post-stroke rehabilitation with the Rutgers ankle system: A case study,” Presence-Teleoper. Virtual Environ. 10(4), 416–430 (2001).CrossRefGoogle Scholar
Boian, R. F., Bouzit, M., Burdea, G. C., Lewis, J. and Deutsch, J. E., “Dual Stewart Platform Mobility Simulator,” Proceedings of the 2005 IEEE 9th International Conference on Rehabilitation Robotics, Chicago, IL, USA (2005) pp. 550–555.Google Scholar
Riener, R., Lünenburger, L. and Colombo, G., “Human-centered robotics applied to gait training and assessment,” J. Rehabil. Res. Dev. 43(5), 679–694 (2006).CrossRefGoogle Scholar
Zhang, M., Xie, S. Q., Li, X., Zhu, G., Meng, W., Huang, X. and Allan, J. V., “Adaptive patient-cooperative control of a compliant ankle rehabilitation robot (CARR) with enhanced training safety,” IEEE Trans. Ind. Electron. 65(2), 1398–1407 (2018).CrossRefGoogle Scholar
Xie, S. Q. and Jamwal, P. K., “An iterative fuzzy controller for pneumatic muscle driven rehabilitation robot,” Expert Syst. Appl. 38(7), 8128–8137 (2011).CrossRefGoogle Scholar
Roy, A., Krebs, H. I., Patterson, S. L., Judkins, T. N., Khanna, I., Forrester, L. W., Macko, R. M. and Hogan, N., “Measurement of Human Ankle Stiffness using the Anklebot,” Proceedings of the 2007 IEEE 10th International Conference on Rehabilitation Robotics, Noordwijk, The Netherlands (2007) pp. 356–363.Google Scholar
Roy, A., Krebs, H. I., Williams, D. J., Bever, C. T., Forrester, L.W., Maeko, R.M. and Hogan, N., “Robot-aided neuro rehabilitation: A novel robot for ankle rehabilitation,” IEEE Trans. Robot. 25(3), 569–582 (2009).CrossRefGoogle Scholar
Stein, J., Krebs, H. I., Frontera, W. R., Fasoli, S. E., Hughes, R. and Hogan, N., “Comparison of two techniques of robot-aided upper limb exercise training after stroke,” Am. J. Phys. Med. Rehabil. 83(9), 720–728 (2004).CrossRefGoogle Scholar
Azcaray, H., Blanco, A., García, C., Adam, M., Reyes, J., Guerrero, G. and Guzmán, C., “Robust GPI control of a new parallel rehabilitation robot of lower,” Int. J. Control Autom. Syst. 16(5), 2384–2392 (2018).CrossRefGoogle Scholar
Bachir, S. and Duvanaud, C., “Linearization of radio frequency amplifiers using nonlinear Internal Model Control method,” AEU-Int. J. Electron. Commun. 65(6), 495–501 (2011).CrossRefGoogle Scholar
Wen, S., Zhang, B., Hao, P., Lam, H. K. and Wang, H., “Fuzzy fractional order force control of 6PUS-UPU redundantly actuated parallel robot based on inner model position control structure,” Eng. Appl. Artif. Intell. 65, 200–211 (2017).CrossRefGoogle Scholar
Zhou, C. and Sagarakis, N., “On the comprehensive kinematics analysis of a humanoid parallel ankle mechanism,” J. Mech. Robot. 10(5), 051015 (2018).CrossRefGoogle Scholar
Eilenberg, M. F., Geyer, H. and Herr, H., “Control of a powered ankle-foot prosthesis based on a neuromuscular model,” IEEE Trans. Neural Syst. Rehabil. Eng. 18(2), 164–173 (2010).CrossRefGoogle Scholar
Park, Y. L., Chen, B. R., Pérez-Arancibia, O. N., Young, D., Stirling, L., Wood, R. J., Goldfield, E. C. and Nagpal, R., “Design and control of a bio-inspired soft wearable robotic device for ankle–foot rehabilitation,” Bioinspir. Biomim. 16(5), 200–211 (2017).Google Scholar
Dai, J. S., Zhao, T. and Nester, C., “Sprained ankle physiotherapy based mechanism synthesis and stiffness analysis of a robotic rehabilitation device,” Auton. Robot. 16(2), 207–218 (2004).CrossRefGoogle Scholar
Jamwal, P. K., Hussain, S. and Xie, S. Q., “Three-stage design analysis and multicriteria optimization of a parallel ankle rehabilitation robot using genetic algorithm,” IEEE Trans. Autom. Sci. Eng. 12(4), 1433–1446 (2015).CrossRefGoogle Scholar
Jamwal, P. K., Xie, S. Q., Hussain, S. and Parsons, J. G., “An adaptive wearable parallel robot for ankle injury treatments,” IEEE-ASME Trans. Mechatron. 19(1), 64–75 (2014).CrossRefGoogle Scholar
Wang, L., Zhu, H. and Chen, Z., “Modeling and Control of Ankle Rehabilitation Robot with Nonlinear Factors,” Proceeding of the IEEE International Conference on Information and Automation, Hailar, China (2014) pp. 64–75.Google Scholar
Li, Z., Fan, D. and Fan, S., “Lugre-model-based friction compensation in direct-drive inertially stabilized platforms,” 6th IFAC Symp. Mechatron. Syst. 46(5), 636–642 (2013).Google Scholar
Sobczyk, M. R., Gervini, V. I., Perondi, E. A. and Cunha, M. A. B., “A continuous version of the Lugre friction model applied to the adaptive control of a pneumatic servo system,” J. Frankl. Inst.-Eng. Appl. Math. 353(13), 3021–3039 (2016).CrossRefGoogle Scholar
Lu, L., Yao, B., Wang, Q. and Chen, Z., “Adaptive robust control of linear motors with dynamic friction compensation using modified LuGre model,” Automatica 45(12), 2890–2896 (2009).CrossRefGoogle Scholar
Johanastrom, K. and Canudas-de-Wit, C., “Revisiting the LuGre friction model,” IEEE Control Syst. Mag. 28(6), 101–114 (2008).CrossRefGoogle Scholar
DeWit, C. C., Olsson, H., Astrom, K. J. and Lischinsky, P., “A newmodel for control of systems with friction,” IEEE Trans. Autom. Control 40(3), 419–425 (1995).Google Scholar
Xiong, Y. and Li, Y., “Modified internal model control scheme for the drive part with elastic joints in robotic system,” J. Intell. Robot. Syst. 79(3–4), 475–485 (2015).CrossRefGoogle Scholar
Li, Q., Poo, A. N. and Lim, C. M., “Internal model based fault tolerant control of a robot manipulator,” Mechatronics 6(5), 571–590 (1996).CrossRefGoogle Scholar
Wang, Q., Bi, Q. and Zhang, Y., “Partial internal model control,” IEEE Trans. Ind. Electron. 48(5), 976–982 (2001).CrossRefGoogle Scholar
Cui, P., Zhang, D., Yang, S. and Li, H., “Friction compensation based on time delay control and internal model control for gimbal system in MSCMG,” IEEE Trans. Ind. Electron. 64(5), 3798–3807 (2016).CrossRefGoogle Scholar
Zhu, Q., Yin, Z., Zhang, Y., Niu, J., Li, Y. and Zhong, Y., “Research on two-degree-of-freedom internal model control strategy for induction motor based on immune algorithm,” IEEE Trans. Ind. Electron. 63(3), 1981–1992 (2016).CrossRefGoogle Scholar
Camposgaona, D., Penaalzola, R., Monroymorales, J. L., Ordonez, M., Anayalara, O. and Leithead, W., “Fast selective harmonic mitigation in multifunctional inverters using internal model controllers and synchronous reference frames,” IEEE Trans. Ind. Electron. 64(8), 6338–6349 (2017).CrossRefGoogle Scholar