Hostname: page-component-669899f699-g7b4s Total loading time: 0 Render date: 2025-04-27T14:42:29.990Z Has data issue: false hasContentIssue false
  • English
  • Français

Fuzzy radial-based impedance controller design for lower limb exoskeleton robot

Published online by Cambridge University Press:  28 September 2022

Peng Zhang ,
Junxia Zhang Open the ORCID record for Junxia Zhang [Opens in a new window]  and
Ahmed Elsabbagh
Peng Zhang
Affiliation:
Tianjin University of Science & Technology, Dagunan Road, Tianjin, China Tianjin Key Laboratory for Integrated Design and Online Monitor Center of Light Design and Food Engineering Machinery Equipment, Tianjin, China
Junxia Zhang*
Affiliation:
Tianjin University of Science & Technology, Dagunan Road, Tianjin, China Tianjin Key Laboratory for Integrated Design and Online Monitor Center of Light Design and Food Engineering Machinery Equipment, Tianjin, China
Ahmed Elsabbagh
Affiliation:
School of Design and Production Engineering, Ain shams University, Cairo, Egypt
*
*Corresponding author: E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The lower extremity rehabilitation exoskeleton is mainly used to help patients with movement disorders complete rehabilitation training. For the human-machine interaction problem of the lower limb rehabilitation exoskeleton, a fuzzy radial-based impedance (RBF-FVI) controller is proposed in this study. A six degree of freedom (DOF) lower extremity rehabilitation exoskeleton was developed, and the human-machine coupling dynamics model was established. To realize the compliance control of the human-machine coupling system, a novel RBF-FVI controller is designed, which includes an inner-loop fuzzy position control module and an outer-loop impedance control module. The inner-loop fuzzy position control module is mainly used to achieve the tracking control of the desired training trajectory and position adjustment amount. The outer-loop impedance control module regulates the impedance parameters and compensates for the uncertainty terms. The superiority of the proposed controller in trajectory following is verified through simulation and comparison tests. The hardware test of the human-machine coupling system was carried out, and the test results showed that the subject and the exoskeleton system could realize a coordinated and smooth movement.

Keywords

exoskeleton robotcompliance controlhuman-machine interactiontrajectory following
Type
Research Article
Information
Robotica , Volume 41 , Issue 1 , January 2023 , pp. 326 - 345
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

, X. Zhao, , X. Tan and , B. Zhang, “Development of soft lower extremity exoskeleton and its key technologies: A survey,” Robot 42, 365–384 (2020).Google Scholar
, R. Bogue, “Robotic exoskeletons: A review of recent progress,” Ind. Robot. 42(1), 5–10 (2015).CrossRefGoogle Scholar
de Looze, T. Bosch, M. P. and , F. Krause, “Exoskeletons for industrial application and their potential effects on physical work load,” Ergon 59, 671–681 (2015).Google Scholar
, G. Zhang, , G. Liu and , S. Ma, “Biomechanical design of escalading lower limb exoskeleton with novel linkage joints,” Technol. Health Care 25(9), 267–273 (2017).CrossRefGoogle Scholar
Mokhtari, M., Taghizadeh, M. and Mazare, M., “Hybrid adaptive robust control based on CPG and ZMP for a lower limb exoskeleton,” Robotica 39(2), 181–199 (2021).CrossRefGoogle Scholar
Zhang, P. and Zhang, J., “Lower limb exoskeleton robots’ dynamics parameters identification based on improved beetle swarm optimization algorithm,” Robotica 40(8), 2716–2731 (2022).CrossRefGoogle Scholar
Zhang, P. and Zhang, J., “Motion generation for walking exoskeleton robot using multiple dynamic movement primitives sequences combined with reinforcement learning,” Robotica 40(8), 2732–2747 (2022).CrossRefGoogle Scholar
Ferraresi, C., Muscolo, G. G., De Benedictis, C., Paterna, M. and Gisolo, S. M., Design and modeling of a novel pneumatic passive upper limb exoskeleton based on McKibben artificial muscle (2021).Google Scholar
Penzlin, B., “Design and first operation of an active lower limb exoskeleton with parallel elastic actuation,” Actuators 10(4), 75 (2021).CrossRefGoogle Scholar
Cao, X. and Wu, W., “Research on impact resistance and active-passive compliance control of rope-driven joint unit,” J. Braz. Soc. Mech. Sci. 43(9), 1–21 (2021).Google Scholar
Qian, W., Ai, L., Zhou, Z., Liao, J., Xiao, X. and Guo, Z., “Adaptive Impedance Control of A 6-DOF Cable-Driven Compliant Upper Limb Rehabilitation Robot,” In: 2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) (IEEE, 2021).CrossRefGoogle Scholar
Lee, H. Y., Park, J. H. and Kim, T.-W., “Comparisons between Locomat and Walkbot robotic gait training regarding balance and lower extremity function among non-ambulatory chronic acquired brain injury survivors,” Medicine 100(18), e25125 (2021).CrossRefGoogle ScholarPubMed
Li, Z., Yuan, Y., Luo, L., Su, W., Zhao, K., Xu, C. and Pi, M., “Hybrid brain/muscle signals powered wearable walking exoskeleton enhancing motor ability in climbing stairs activity,” IEEE Trans. Med. Robot. Bion. 1(4), 218–227 (2019).CrossRefGoogle Scholar
Kazerooni, H., “The Berkeley lower extremity exoskeleton,” J. Dyn. Syst.-T ASME 128, 9–15 (2006).Google Scholar
Ekso Bionics (2013), http://www.eksobionics.com/ekso.Google Scholar
Chen, L., Wang, C., Song, X., Wang, J., Zhang, T. and Li, X., “Dynamic trajectory adjustment of lower limb exoskeleton in swing phase based on impedance control strategy,” Proc. Inst. Mech. Eng. I J. Syst. Cont. Eng. 234(10), 1120–1132 (2020).Google Scholar
Luna, L., García, I., Mendoza, M., Dorantes-Méndez, G., Mejía-Rodríguez, A. and Bonilla, I., “EMG-Based Kinematic Impedance Control of A Lower-Limb Exoskeleton,” In: Latin American Conference on Biomedical Engineering (Springer, Cham, 2019).Google Scholar
Wang, Y., “An Impedance Control Method of Lower Limb Exoskeleton Rehabilitation Robot Based on Predicted Forward Dynamics,” In: 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) (IEEE, 2020).Google Scholar
Mokhtari, M., Taghizadeh, M. and Mazare, M., “Impedance control based on optimal adaptive high order supertwisting sliding mode for a 7-DOF lower limb exoskeleton,” Meccanica 56(3), 535–548 (2021).CrossRefGoogle Scholar
Chen, J., Li, Y. and Zeng, J., “Active interactive genetic control for lower limb rehabilitation robots with uncertainties,” In: 2021 40th Chinese Control Conference (CCC) 40, 491–496 (2021). doi: 10.23919/CCC52363.2021.9549638.CrossRefGoogle Scholar
Huo, W., Moon, H., Alouane, M. A., Bonnet, V., Huang, J., Amirat, Y. and Mohammed, S., “Impedance modulation control of a lower-Limb exoskeleton to assist sit-to-stand movements,” IEEE Trans. Robot. 38(2), 1230–1249 (2021).CrossRefGoogle Scholar
Sharp, I., Huang, F. and Patton, J., “Visual error augmentation enhances learning in three dimensions,” J. Neuroeng. Rehabil. 8(1), 1–6 (2011).CrossRefGoogle ScholarPubMed
Akdoğan, E., Taçgin, E. and Adli, M. A., “Knee rehabilitation using an intelligent robotic system,” J. Intell. Manuf. 20(2), 195–202 (2009).CrossRefGoogle Scholar
Chen, Z., Guo, Q., Yan, Y. and Shi, Y., “Model identification and adaptive control of lower limb exoskeleton based on neighborhood field optimization,” Mechatronics 81(2), 102699 (2022).CrossRefGoogle Scholar
Qian, W., Ai, L., Zhou, Z., Liao, J., Xiao, X. and Guo, Z., “Adaptive Impedance Control of A 6-DOF Cable-Driven Compliant Upper Limb Rehabilitation Robot,” In: 2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) (IEEE, 2021).CrossRefGoogle Scholar
Baigzadehnoe, B., Rahmani, Z., Khosravi, A. and Rezaie, B., “On position/force tracking control problem of cooperative robot manipulators using adaptive fuzzy backstepping approach,” ISA Trans. 70(10), 432–444 (2017).CrossRefGoogle ScholarPubMed
Stojcsics, Dániel, “Fuzzy Controller for Small Size Unmanned Aerial Vehicles,” In: 2012 IEEE 10th International Symposium on Applied Machine Intelligence and Informatics (SAMI) (IEEE, 2012).CrossRefGoogle Scholar
Li, Z., Li, X., Li, Q., Su, H., Kan, Z. and He, W., “Human-In-the-Loop control of soft exosuits using impedance learning on different terrains,” IEEE Trans. Robot., early access, 1–15 (2022).Google Scholar
Yu, X., Li, B., He, W., Feng, Y., Cheng, L. and Silvestre, C., “Adaptive-constrained impedance control for human-robot co-transportation,” IEEE Trans. Cybern., early access, 1–13 (2021).Google Scholar
Brahmi, B., Driscoll, M., El Bojairami, I. K., Saad, M. and Brahmi, A., “Novel adaptive impedance control for exoskeleton robot for rehabilitation using a nonlinear time-delay disturbance observer,” ISA Trans. 108, 381–392 (2021).CrossRefGoogle ScholarPubMed
Zhang, P., Zhang, J. and Zhang, Z., “Design of RBFNN-based adaptive sliding mode control strategy for active rehabilitation robot,” IEEE Access 8, 155538–155547 (2020).CrossRefGoogle Scholar
Zhang, J., Cheah, C. C. and Collins, S. H., “Experimental Comparison of Torque Control Methods on an Ankle Exoskeleton During Human Walking,” In: 2015 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2015).CrossRefGoogle Scholar
Li, H., Liu, C., Yan, L., Zhang, B., Li, D. and Zhang, Q., “Impedance control and joint test study of upper limb exoskeleton robot,” J. Mech. Eng. 56(19), 200–209 (2020).Google Scholar
Zhang, Y. M., Wu, Q. C., Chen, B., Wu, H. T. and Liu, H. R., “Fuzzy neural network impedance control of a soft rehabilitation exoskeleton robot for lower limbs,” Robotics 42, 477–484 (2020).Google Scholar