JRM Vol.33 No.1 pp. 88-96
doi: 10.20965/jrm.2021.p0088


Human-Like Robust Adaptive PD Based Human Gait Tracking for Exoskeleton Robot

Aihui Wang*, Ningning Hu**, Jun Yu***, Junlan Lu*, Yifei Ge*, and Yan Wang*

*School of Electric and Information Engineering, Zhongyuan University of Technology
No.41 Zhongyuan Road, Zhengzhou 450007, China

**School of Mechatronic Engineering and Automation, Shanghai University
No.99 Shangda Road, Shanghai 200444, China

***Zhongyuan-Petersburg Aviation College, Zhongyuan University of Technology
No.41 Zhongyuan Road, Zhengzhou 450007, China

January 20, 2020
September 12, 2020
February 20, 2021
motion capture, human gait trajectory, robust adaptive PD, parameters identification
Human-Like Robust Adaptive PD Based Human Gait Tracking for Exoskeleton Robot

NOKOV 3D motion capture system

For patients with dyskinesias caused by central nervous system diseases such as stroke, in the early stage of rehabilitation training, lower limb rehabilitation robots are used to provide passive rehabilitation training. This paper proposed a human-like robust adaptive PD control strategy of the exoskeleton robot based on healthy human gait data. When the error disturbance is bounded, a human-like robust adaptive PD control strategy is designed, which not only enables the rehabilitation exoskeleton robot to quickly track the human gait trajectory obtained through the 3D NOKOV motion capture system, but also can well identify the structural parameters of the system and avoid excessively initial output torque for the robot. MATLAB simulation verifies that the proposed method has a better performance to realize tracking the experimental trajectory of human movement and anti-interference ability under the condition of ensuring global stability for a lower limb rehabilitation exoskeleton robot.

Cite this article as:
Aihui Wang, Ningning Hu, Jun Yu, Junlan Lu, Yifei Ge, and Yan Wang, “Human-Like Robust Adaptive PD Based Human Gait Tracking for Exoskeleton Robot,” J. Robot. Mechatron., Vol.33, No.1, pp. 88-96, 2021.
Data files:
  1. [1] X. Chen, “Data from the second national sample survey of disabled persons,” Chinese J. of Health, Vol.68, pp. 1-3, 2008.
  2. [2] C. Xie, G. Xu, and X. Liu, “Research progress of early rehabilitation after stroke,” Rehabilitation Theory and Practice in China, Vol.10, No.15, pp. 908-912, 2009.
  3. [3] H. Woldag and H. Hummelsheim, “Evidence-based physiotherapeutic concepts for improving arm and hand function in stroke patients,” J. of Neurology, Vol.249, No.5, pp. 518-528, 2002.
  4. [4] P. Connor and A. Ross, “Biometric recognition by gait: A survey of modalities and features,” Computer Vision and Image Understanding, Vol.167, pp. 1-27, 2018.
  5. [5] A. G. Kirk, J. F. O’Brien, and D. A. Forsyth, “Skeletal parameter estimation from optical motion capture data,” Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR’05), pp. 782-788, 2005.
  6. [6] H. Ren, D.-X. Liu, N. Li, Y. He, Z. Yan, and X. Wu, “On-line Dynamic Gait Generation Model for Wearable Robot with User’s Motion Intention,” Proc. IEEE Int. Conf. on Information and Automation (ICIA), pp. 347-352, 2018.
  7. [7] P. Sun, W. Zhang, S. Wang, and H. Chang, “Interaction Forces Identification Modeling and Tracking Control for Rehabilitative Training Walker,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.2, pp. 183-195, 2019.
  8. [8] T. Henmi, “Control Parameters Tuning Method of Nonlinear Model Predictive Controller Based on Quantitatively Analyzing,” J. Robot. Mechatron., Vol.28, No.5, pp. 695-701, 2016.
  9. [9] Munadi and T. Naniwa, “Experimental Verification of Adaptive Dominant Type Hybrid Adaptive and Learning Controller for Trajectory Tracking of Robot Manipulators,” J. Robot. Mechatron., Vol.25, No.4, pp. 737-747, 2013.
  10. [10] X. Li, “Data acquisition system and recognition design of human motion,” Computer Technology and Automation, 2019.
  11. [11] Z. Huang and C. Fang, “Research on robot perception system of lower limb exoskeleton based on multi-information fusion,” J. of Engineering Design, pp. 159-166, 2018.
  12. [12] A. De Luca, B. Siciliano, and L. Zollo, “PD control with on-line gravity compensation for robots with elastic joints: Theory and experiments,” Automatica, Vol.41, No.10, pp. 1809-1819, 2005.
  13. [13] L. Zollo, B. Siciliano, A. De Luca, and E. Guglielmelli, “PD control with on-line gravity compensation for robots with flexible links,” Proc. European Control Conf. (ECC), pp. 4365-4370, 2007.
  14. [14] G. Yi and X. Zhang, “Adaptive control of trajectory tracking of lower limb rehabilitation robot with uncertain model,” J. of Electronic Measurement and Instrumentation, 2016.
  15. [15] Q. Chen and H. Zhang, “A simple robust adaptive trajectory tracking control for robotic manipulators,” J. of Huazhong University of Science and Technology (Nature Science Edition), pp. 63-65, 2004.
  16. [16] Y. Su and C. Zheng, “Nonlinear PD plus control for global asymptotic tracking of robot manipulators,” Control and Decision, Vol.24, No.11, 2009.
  17. [17] A. Wang, Z. Ma, and J. Luo, “Operator-Based Robust Nonlinear Control Analysis and Design for a Bio-Inspired Robot Arm with Measurement Uncertainties,” J. Robot. Mechatron., Vol.31, No.1, pp. 104-109, 2019.
  18. [18] Z.-H. Jiang, S. Nie, and T. Ishita, “A neural network and linear feedback based trajectory control method for robot manipulators,” Proc. 7th World Congress on Intelligent Control and Automation, pp. 1603-1608, 2008.
  19. [19] J. Lee, P. H. Chang, and M. Jin, “An Adaptive Gain Dynamics for Time Delay Control Improves Accuracy and Robustness to Significant Payload Changes for Robots,” IEEE Trans. on Industrial Electronics, Vol.67, No.4, pp. 3076-3085, 2020.
  20. [20] Y. Du, S. Qiu, P. Xie, Z.-H. Guo, X.-G. Wu, and X.-L. Li, “Adaptive Interaction Control for Lower limb Rehabilitation Robots,” Acta Automatica Sinica, Vol.44, No.4, pp. 743-750, 2018.
  21. [21] M. F. Ruzaij, S. Neubert, N. Stoll, and K. Thurow, “Hybrid Voice Controller for Intelligent Wheelchair and Rehabilitation Robot Using Voice Recognition and Embedded Technologies,” J. Adv. Comput. Intell. Intell. Inform., Vol.20, No.4, pp. 615-622, 2016.
  22. [22] S. Hagane, L. K. R. Ardila, T. Katsumata, V. Bonnet, P. Fraisse, and G. Venture, “Adaptive Generalized Predictive Controller and Cartesian Force Control for Robot Arm Using Dynamics and Geometric Identification,” J. Robot. Mechatron., Vol.30, No.6, pp. 927-942, 2018.

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Last updated on Mar. 01, 2021