Abstract
Aiming at the problems of poor size adjustability and low joint tracking accuracy of lower limb exoskeleton rehabilitation robot (LLERR), a variable size lower limb exoskeleton rehabilitation robot (VSLLERR) was designed by UG NX software based on human body size data. Then, the kinematics model of VSLLERR was established by DH method, and the motion space of VSLLERR was analyzed. In addition, the dynamics model of VSLLERR was established by Lagrangian energy method, and the general nonlinear friction model was designed to modify and improve the accuracy of dynamics model. Then, the PID and reaching law (RL) controllers of VSLLERR were designed by SIMULINK. Furthermore, the joint tracking accuracy of the two controllers and the influence of RL controller parameters on tracking accuracy were studied by simulation experiment. The results indicate that the joint angle and joint angular velocity tracking accuracy of RL controller are higher than that of PID controller. In addition, appropriate parameters (c1, c2, k, ε, ϕ) can improve the tracking accuracy of VSLLERR.
Similar content being viewed by others
References
X. Tang et al., A wearable lower limb exoskeleton: reducing the energy cost of human movement, Micromachines, 13 (6) (2022) 900.
Y. Ding, L. Tu, Y. Liu, J. Zhang and M. Shuai, Progress of wearable lower-limb exoskeleton rehabilitation robots, ROBOT, 44 (5) (2022) 522–532.
X. Shi, H. Wang, L. Sun, F. Gao and Z. Xu, Design and dynamic analysis of an exoskeletal lower limbs rehabilitation robot, J. of Mechanical Engineering, 50 (3) (2014) 41–48.
G. M. Bryan et al., A hip-knee-ankle exoskeleton emulator for studying gait assistance, The International J. of Robotics Research, 40 (4–5) (2021) 722–746.
Y. Zhang et al., A self-adaptive-coefficient-double-power sliding mode control method for lower limb rehabilitation exoskeleton robot, Applied Sciences, 11 (21) (2021) 10329.
Y. Li et al., Dynamic parameter identification of a human exoskeleton system with the motor torque data, IEEE Transactions on Medical Robotics and Bionics, 4 (1) (2021) 206–218.
M. Gao et al., Electrically driven lower limb exoskeleton rehabilitation robot based on anthropomorphic design, Machines, 10 (4) (2022) 266.
Y. Xu and R. Liu, Dynamic modeling of constrained planar multibody systems: a case of lower limbs rehabilitative robot, J. of Mech. Sci. and Tech., 32 (7) (2018) 3389–3394.
Y. Liu, J. Zhang and W. Liao, Dynamic modeling and identification of wearable lower limb rehabilitation exoskeleton robots, 2022 4th International Conference on Control and Robotics (ICCR), Guangzhou, China (2022) 217–221.
J. Yu et al., Musculoskeletal modeling and humanoid control of robots based on human gait, Peer J Computer Science, 7 (3) (2021) 657.
G. Li et al., Dynamic analysis and design of a multipurpose lower limb exoskeleton for rehabilitation, International J. of Advanced Robotic Systems, 19 (6) (2022) 1–23.
O. Baser, H. Kizilhan and E. Kilic, Employing variable impedance (stiffness/damping) hybrid actuators on lower limb exoskeleton robots for stable and safe walking trajectory tracking, J. of Mech. Sci. and Tech., 34 (6) (2020) 2597–2607.
K. He and L. Chen, Research of fuzzy PID control for lower limb wearable exoskeleton robot, 2021 4th International Conference on Intelligent Autonomous Systems (ICoIAS), Wuhan, China (2021) 385–389.
K. H. Al-Waeli et al., Offline ANN-PID controller tuning on a multi-joints lower limb exoskeleton for gait rehabilitation, IEEE Access, 9 (2021) 107360–107374.
T. Lee, I. Kim and Y. S. Baek, Design of a 2DoF ankle exoskeleton with a polycentric structure and a bi-directional tendon-driven actuator controlled using a PID neural network, Actuators, 10 (1) (2021) 9.
R. Roy et al., Investigation of 2DOF PID controller for physiotherapeutic application for elbow rehabilitation, Applied Sciences, 11 (18) (2021) 8617.
N. Qiao, L. Wang, M. Liu and Z. Wang, The sliding mode controller with improved reaching law for harvesting robots, J. of Intelligent and Robotic Systems, 104 (2022) 9.
P. Ji, C. Li and F. Ma, Sliding mode control of manipulator based on improved reaching law and sliding surface, Mathematics, 10 (2022) 1935.
B. Brahmi et al., Novel adaptive reaching law for sliding mode control of an upper limb exoskeleton robot, 2020 IEEE Region 10 Symposium (TENSYMP), Dhaka, Bangladesh (2020) 1432–1437.
B. O. Mushage, J. C. Chedjou and K. Kyamakya, Fuzzy neural network and observer-based fault-tolerant adaptive nonlinear control of uncertain 5-DOF upper-limb exoskeleton robot for passive rehabilitation, Nonlinear Dynamics, 87 (3) (2017) 1–17.
M. H. Rahman, M. Saad, J.-P. Kenné and P. S. Archambault, Control of an exoskeleton robot arm with sliding mode exponential reaching law, International J. of Control, Automation, and Systems, 11 (1) (2013) 92–104.
Y. Xu, R. Liu, J. Liu and J. Zhang, A novel constraint tracking control with sliding mode control for industrial robots, International J. of Advanced Robotic Systems, 18 (4) (2021) 1–9.
S. Long, X. Dang, S. Sun, Y. Wang and M. Gui, A novel sliding mode momentum observer for collaborative robot collision detection, Machines, 10 (2022) 818.
J. Narayan and S. K. Dwivedy, Robust LQR-based neural-fuzzy tracking control for a lower limb exoskeleton system with parametric uncertainties and external disturbances, Applied Bionics and Biomechanics, 2021 (2021) 5573041.
P. Yang, K. Feng, Y. Ding and Z. Shen, Fast terminal sliding mode control based on finite-time observer and improved reaching law for aerial robots, Actuators, 11 (2022) 258.
D. Huamanchahua et al., Kinematic analysis of an 4 DOF upper-limb exoskeleton, 2021 IEEE 12th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), New York, USA (2021) 0914–0923.
H. Hu et al., Research on the correlation of Chinese adult human body size data, Chinese J. of Ergonomics, 20 (3) (2014) 49–53.
Y. Tu et al., Adaptive admittance control of man-robot interaction force for lower limb exoskeleton rehabilitation robot, J. of Xi’an Jiaotong University, 53 (6) (2019) 9–16.
S. Duan et al., Inverse of key parameters of nonlinear friction model of robot joints, Chinese J. of Theoretical and Applied Mechanics, 54 (11) (2022) 3189–3202.
S. Duan, C. Li, X. Han and G. Liu, Forward-inverse dynamics analysis of robot arm trajectories and development of a nonlinear friction model for robot joints, J. of Mechanical Engineering, 56 (9) (2020) 18–28.
J. Liu, Sliding Mode Variable Structure Control MATLAB Simulation, Tsinghua University Press, Beijing, China (2014).
M. Rahmani and M. H. Rahman, Adaptive neural network fast fractional sliding mode control of a 7-DOF exoskeleton robot, International J. of Control, Automation and Systems, 18 (1) (2020) 124–133.
Z. Xie, M. Xiang and L. Jiang, Design and kinematics analysis simulation of power assisted lower extremity exoskeleton, Machinery Design and Manufacture, 10 (2020) 21–24.
Acknowledgments
This work is supported by the Science And Technology Bureau Of Leshan Town (Design of a Multi-posture Lower Limb Rehabilitation Robot Based on the D-H Method and TRIZ Theory), China.
Author information
Authors and Affiliations
Corresponding author
Additional information
Ruqiang Mou is a lecturer in the Department of Automation Engineering, Engineering & Technical College of Chengdu University of Technology, Leshan, China. He received his M.D. in Mechatronic Engineering from Sichuan University in 2016. His research interests include robot mechanism and control, vehicle dynamics and vibration control, mechatronics design.
Le Li is an engineer of the Substation Operation and Maintenance Department, State Grid Leshan Power Supply Company, Leshan, China. She received her M.D. in Electrical Engineering from Sichuan University in 2016. Her research interests include intelligent robots, power patrol robots, predictive control.
Rights and permissions
About this article
Cite this article
Mou, R., Li, L. Research on design and trajectory tracking control of a variable size lower limb exoskeleton rehabilitation robot. J Mech Sci Technol 38, 389–400 (2024). https://doi.org/10.1007/s12206-023-1232-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12206-023-1232-9