Dynamic Parameter Identification and Gait Tracking of Lower Limb Exoskeleton Robot

Liu Yang; Peng Shi-guo; Ma Hong-zhi; Liao Wei-xin

doi:10.12052/gdutxb.220014

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Dynamic Parameter Identification and Gait Tracking of Lower Limb Exoskeleton Robot

更新时间：2026-05-27

- Dynamic Parameter Identification and Gait Tracking of Lower Limb Exoskeleton Robot
- Vol. 39, Issue 6, Pages: 44-52(2022)
- 作者机构：
  
  1. 广东工业大学自动化学院,广东,广州,510006
  2. 香港中文大学机械与自动化系,香港,999077
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.220014
  CLC： TP242
- Published：2022
- 稿件说明：
移动端阅览
刘洋, 彭世国, 马宏志, 廖维新. 下肢外骨骼机器人动力学参数辨识与步态跟踪[J]. 广东工业大学学报, 2022, 39(6): 44-52.

Liu Yang, Peng Shi-guo, Ma Hong-zhi, et al. Dynamic Parameter Identification and Gait Tracking of Lower Limb Exoskeleton Robot[J]. 2022, 39(6): 44-52.
刘洋, 彭世国, 马宏志, 廖维新. 下肢外骨骼机器人动力学参数辨识与步态跟踪[J]. 广东工业大学学报, 2022, 39(6): 44-52. DOI： 10.12052/gdutxb.220014.

Liu Yang, Peng Shi-guo, Ma Hong-zhi, et al. Dynamic Parameter Identification and Gait Tracking of Lower Limb Exoskeleton Robot[J]. 2022, 39(6): 44-52. DOI： 10.12052/gdutxb.220014.

摘要

为了提高下肢外骨骼机器人步态轨迹跟踪的精度，对于下肢外骨骼二连杆动力学模型，提出一种静态与动态结合的参数辨识的实验方法，并结合穿戴者人体参数，得到人机协同系统精确的动力学模型。采用基于模型上界的滑模控制，并引入低通滤波器，进行MATLAB步态跟踪仿真。经仿真表明，髋关节和膝关节转矩的实验测量值与理论计算值的波形基本一致，动力学参数辨识结果正确；基于滑模控制的人机协同系统能够实现髋关节和膝关节对参考步态轨迹的精准跟踪，低通滤波器能够有效减小滑模控制引起的高频抖振。这为下肢外骨骼动力学参数辨识提供了一种解决方案，为基于模型的控制方法提供了一种参考模型，为下肢外骨骼人机协同系统的步态轨迹精准跟踪提供了一种参考方法。

Abstract

To improve the tracking accuracy of the gait trajectory of the lower limb exoskeleton robot (LLER)

an experimental method of parameter identification is proposed for the LLER’s two-link dynamic model

including static and dynamic experiment. Combined with the wearer’s human parameters

the accurate dynamic model of the LLER human-machine collaborative system is deduced. The sliding mode control (SMC) with the upper bound of the model and a low-pass filter are adopted to track the gait trajectory precisely by MATLAB. The simulation shows that the waveforms of the hip and knee torques measured by experiment are basically consistent with the theoretical values by calculation

and the dynamic parameter identification results are correct. The human-machine collaborative system based on SMC can realize the accurate tracking of the reference gait trajectories of the hip and knee joints

and the low-pass filter can effectively reduce the high-frequency chattering caused by SMC. This research provides a solution for the identification of the dynamic parameters of LLER

a reference model for the model-based control method

and a reference method for precisely tracking the gait trajectory of LLER human-machine collaborative system

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references

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