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Study on the design and control method of a wire-driven waist rehabilitation training parallel robot

Published online by Cambridge University Press:  28 March 2022

Wang Yuqi Open the ORCID record for Wang Yuqi [Opens in a new window] ,
Cao Jinjiang ,
Geng Ranran ,
Zhou Lei  and
Wang Lei
Wang Yuqi*
Affiliation:
School of Automation, Nanjing Institute of Technology, Nanjing, China
Cao Jinjiang
Affiliation:
School of Automation, Nanjing Institute of Technology, Nanjing, China
Geng Ranran
Affiliation:
Industrial Center, Nanjing Institute of Technology, Nanjing, China
Zhou Lei
Affiliation:
School of Automation, Nanjing Institute of Technology, Nanjing, China
Wang Lei
Affiliation:
School of Automation, Nanjing Institute of Technology, Nanjing, China
*
Corresponding author. E-mail: [email protected]
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Abstract

With the increasing demand for rehabilitation and the lack of professional rehabilitation personnel, robot-assisted rehabilitation technology plays an increasingly important role in neurological rehabilitation. In order to recover the exercise ability of patients with waist injury, a new type of wire-driven waist rehabilitation training parallel robot (WWRTPR) is designed. According to the motion trajectory planning of waist rehabilitation training, two coordinate systems are established: moving coordinate system and static coordinate system. The inverse kinematics modeling analysis is carried out, and the dynamic model of the robot is established by using Newton–Euler method. An intelligent control method of force/position hybrid control based on radial basis function neural network is proposed. The stability of the closed-loop system is analyzed, and the results show that WWRTPR tends to be stable. The simulation analysis of rehabilitation training on WWRTPR is carried out, and the simulation results show that the proposed intelligent control method can effectively control the robot system, which provides a reference for the development of a flexible intelligent rehabilitation training robot.

Keywords

wire-driven rehabilitation training robotprototype designmodeling analysisintelligent controlsimulation analysis
Type
Research Article
Information
Robotica , Volume 40 , Issue 10 , October 2022 , pp. 3499 - 3513
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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References

Zhang, B., Zhang, F., Zhou, F., Shang, W. and Cong, S., “Dual-space adaptive synchronization control of cable-driven parallel robots,” Robot 42(2), 139–147 (2020).Google Scholar
You, H., Shang, W., Zhang, B., Zhang, F. and Cong, S., “Trajectory tracking control of cable-driven parallel robots by using high-speed vision,” J. Mech. Eng. 55(5), 19–26 (2019).CrossRefGoogle Scholar
Zou, Y., Wang, N., Liu, K. and Geng, X., “Design and analysis of movable cable-driven lower limb rehabilitation robot,” J. Huazhong Univ. Sci. Technol. 47(1), 22–26, 38 (2019).Google Scholar
Daniele, C., Matteo, R. and Giuseppe, C., “Cube, a cable-driven device for limb rehabilitation,” J. Bionic Eng. 16(3), 493–503 (2019).Google Scholar
Huang, X., Yu, H., Wang, J., Dong, Q., Zhang, L., Meng, Q., Li, S. and Wang, D., “Study on the center-driven multiple degrees of freedom upper limb rehabilitation training robot,” J. Biomed. Eng 35(3), 452–459 (2018).Google Scholar
Chen, Q., Zi, B., Sun, Z., Wang, N., Li, S. and Luo, X., “Design, analysis and experimental study of a cable-driven parallel waist rehabilitation robot,” J. Mech. Eng. 54(13), 126–134 (2018).CrossRefGoogle Scholar
Gharatappeh, S., Abbasnejad, G., Yoon, J. and Lee, H., “Control of Cable-Driven Parallel Robot for Gait Rehabilitation, The 12th International Conference on Ubiquitous Robots and Ambient Intelligence pp. 377–381.Google Scholar
Asl, H. J. and Yoon, J., “Stable assist-as-needed controller design for a planar cable-driven robotic system,” Int. J. Control Autom. Syst. 15(6), 2871–2882 (2017).CrossRefGoogle Scholar
Zou, Y., Wang, N., Wang, X., Ma, H. and Liu, K., “Design and experimental research of movable cable-driven lower limb rehabilitation robot,” IEEE Access 7, 2315–2326 (2019).CrossRefGoogle Scholar
Aguirre-Ollinger, G., Narayan, A. and Yu, H., “Phase-synchronized assistive torque control for the correction of kinematic anomalies in the gait cycle,” IEEE Trans. Neural. Syst. Rehabilitation Eng. 27(11), 2305–2314 (2019).CrossRefGoogle ScholarPubMed
Li, X., Yang, Q. and Song, R., “Perform ance-based hybrid control of a cable-driven upper-limb rehabilitation robot,” IEEE Trans. Biomed. Eng. 68(4), 1351–1359(2021).CrossRefGoogle Scholar
Jarrett, C. and McDaid, A. J., “Robust control of a cable-driven soft exoskeleton joint for intrinsic human-robot interaction,” IEEE Trans. Neural Syst. Rehabilitation Eng. 25(7), 976–986 (2017).CrossRefGoogle ScholarPubMed
Chen, Q., Zi, B., Sun, Z., Li, Y. and Xu, Q., “Design and development of a new cable-driven parallel robot for waist rehabilitation,” IEEE/ASME Trans. Mechatron. 24(4), 1497–1507 (2019).CrossRefGoogle Scholar
Zi, B., Yin, G. and Zhang, D., “Design and optimization of a hybrid-driven waist rehabilitation robot,” Sensors 16(12), 2–15 (2016).CrossRefGoogle ScholarPubMed
Vashista, V., Khan, M. and Agrawal, S. K., “A novel approach to apply gait synchronized external forces on the pelvis using A-Tpad to reduce walking effort,” IEEE Robot. Autom. Lett. 1(2), 1118–1124 (2016).CrossRefGoogle ScholarPubMed
Qian, S., Zi, B. and Ding, H., “Dynamics and trajectory tracking control of cooperative multiple mobile cranes,” Nonlinear Dyn. 83(1-2), 89–108 (2016).CrossRefGoogle Scholar
Gao, Z., Wang, X., Wu, J. and Lin, Q., “Hybrid force/pose control of wire-driven parallel suspension system based on stiffness optimization,” Acta Aeronautica et Astronautica Sinica 42, 1–12 (2021).Google Scholar
Jun, J. P., Jin, X., Pott, A., Park, S., Park, J.-O. and Ko, S. Y., “Hybrid position/force control using an admittance control scheme in Cartesian space for a 3-Dof planar cable-driven parallel robot,” Int. J. Control Autom. Syst. 14(4), 1106–1113 (2016).CrossRefGoogle Scholar
Shang, W., Zhang, B., Zhang, B., Zhang, F. and Cong, S., “Synchronization control in the cable space for cable-driven parallel robots,” IEEE Trans. Ind. Electron. 66(6), 4544–4554 (2019).CrossRefGoogle Scholar
He, J.. Dynamic Modeling and Control of the Cable-Driven Parallel Robots (Civil Aviation University of China, Tianjin, 2020).Google Scholar
Shoaib, M., Asadi, E., Cheong, J. and Bab-Hadiashar, A., “Cable driven rehabilitation robots: Comparison of applications and control strategies,” IEEE Access 9, 110396–110420 (2021).CrossRefGoogle Scholar
Ni, W., Li, H., Jiang, Z., Zhang, B. and Huang, Q., “Motion and force control method of 7-Dof cable-driven rehabilitation exoskeleton robot,” Assembly Autom. 38(5), 595–605 (2018).CrossRefGoogle Scholar
Wang, Y., Wang, K., Zhang, Z. and Mo, Z., “Control strategy and experimental research of a cable-driven lower limb rehabilitation robot,” Proc. Inst. Mech. Eng. C-J. Mech. Eng. Sci. 235(13), 2468–2481 (2021).CrossRefGoogle Scholar
Li, X., Yang, Q. and Song, R., “Performance-based hybrid control of a cable-driven upper-limb rehabilitation robot,” IEEE Trans. Biomed. Eng. 68(4), 1351–1359 (2021).CrossRefGoogle ScholarPubMed
Liu, J.. RBF Neural Network Control for Mechanical Systems: Design, Analysis and Matlab Simulation. 2nd edition, Tsinghua University Press, Beijing, 2018) pp. 140–142.Google Scholar
Wang, Y., Sun, W., Xiang, Y. and Miao, S., “Neural network-based robust tracking control for robots,” Intell. Autom. Soft Comput. 15(2), 211–222 (2009).CrossRefGoogle Scholar
Wang, Y., Wang, K., Wang, W., Han, Z. and Zhang, Z., “Appraisement and analysis of dynamical stability of under-constrained cable-driven lower-limb rehabilitation training robot,” Robotica 39(6), 1023–1036 (2021).CrossRefGoogle Scholar
Bahrami, V., Kalhor, A. and Masouleh, M. T., “Dynamic model estimating and designing controller for the 2-DoF planar robot in interaction with cable-driven robot based on adaptive neural network,” J. Intell. Fuzzy Syst. 41(1), 1261–1280 (2021).CrossRefGoogle Scholar
Zake, Z., Chaumette, F., Pedemonte, N. and Caro, S., “Robust 21/2D visual servoing of a cable-driven parallel robot thanks to trajectory tracking,” IEEE Robot. Autom. Lett. 5(2), 660–667 (2020).CrossRefGoogle Scholar
Lou, Y., Lin, H., Quan, P., Wei, D. and Di, S., “Robust adaptive control of fully constrained cable-driven serial manipulator with multi-segment cables using cable tension sensor measurements,” Sensors 21(5), 1–31 (2021).CrossRefGoogle ScholarPubMed