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基于无模型自适应的外骨骼式上肢康复机器人主动交互训练控制方法

王晓峰 李醒 王建辉

王晓峰, 李醒, 王建辉. 基于无模型自适应的外骨骼式上肢康复机器人主动交互训练控制方法. 自动化学报, 2016, 42(12): 1899-1914. doi: 10.16383/j.aas.2016.c160057
引用本文: 王晓峰, 李醒, 王建辉. 基于无模型自适应的外骨骼式上肢康复机器人主动交互训练控制方法. 自动化学报, 2016, 42(12): 1899-1914. doi: 10.16383/j.aas.2016.c160057
WANG Xiao-Feng, LI Xing, WANG Jian-Hui. Active Interaction Exercise Control of Exoskeleton Upper Limb Rehabilitation Robot Using Model-free Adaptive Methods. ACTA AUTOMATICA SINICA, 2016, 42(12): 1899-1914. doi: 10.16383/j.aas.2016.c160057
Citation: WANG Xiao-Feng, LI Xing, WANG Jian-Hui. Active Interaction Exercise Control of Exoskeleton Upper Limb Rehabilitation Robot Using Model-free Adaptive Methods. ACTA AUTOMATICA SINICA, 2016, 42(12): 1899-1914. doi: 10.16383/j.aas.2016.c160057

基于无模型自适应的外骨骼式上肢康复机器人主动交互训练控制方法

doi: 10.16383/j.aas.2016.c160057
基金项目: 

中央高校基本科研业务费专项资金 N150804001

国家自然科学基金 61503070

2015辽宁省博士点专项基金 201501142

详细信息
    作者简介:

    王晓峰 东北大学信息学院自动化研究所博士研究生.2012年获得东北大学信息学院自动化专业学士学位.主要研究方向为上肢康复系统与机器人控制技术.E-mail:wxflamy@yeah.net

    王建辉 东北大学信息学院教授.分别于1982, 1986与1999年在东北大学获得学士, 硕士和博士学位.主要研究方向为智能控制理论与应用.E-mail:wangjianhui@mail.neu.edu.cn

    通讯作者:

    李醒 东北大学流程工业综合自动化国家重点实验室助理教授.2012年获得东北大学电力电子与电子传动专业博士学位.主要研究方向为自适应/鲁棒控制, 运动控制, 智能机器人系统.本文通信作者.E-mail:lixing8245@163.com

Active Interaction Exercise Control of Exoskeleton Upper Limb Rehabilitation Robot Using Model-free Adaptive Methods

Funds: 

Fundamental Research Funds for the Central Universities N150804001

National Natural Science Foundation of China 61503070

2015 Liaoning Province Doctoral Fund 201501142

More Information
    Author Bio:

    Ph. D. candidate at the Institute of Automation, Northeastern University. He received his bachelor degree in automation from Northeastern University in 2012. His research interest covers upper-limb rehabilitation systems and robot control

    Professor at the College of Information Science and Engineering, Northeastern University. She received her bachelor, master and Ph. D. degrees in electrical engineering from Northeastern University in 1982, 1986, and 1999, respectively. Her research interest covers intelligent control theory and its application

    Corresponding author: WANG Jian-Hui Professor at the College of Information Science and Engineering, Northeastern University. She received her bachelor, master and Ph. D. degrees in electrical engineering from Northeastern University in 1982, 1986, and 1999, respectively. Her research interest covers intelligent control theory and its application
  • 摘要: 设计了一种基于无模型自适应的外骨骼式上肢康复机器人主动交互训练控制方法.在机器人与人体上肢接触面安装力传感器采集人机交互力矩信息作为量化的主动运动意图,设计了一种无模型自适应滤波算法使交互力矩变得平滑而连贯;以人机交互力矩为输入,综合考虑机器人末端点与参考轨迹的相对位置和补偿力的信息,设计了人机交互阻抗控制器,用于调节各关节的给定目标速度;设计了将无模型自适应与离散滑模趋近律相结合的速度控制器完成机器人各关节对目标速度的跟踪.仿真结果表明,该控制方法可以实现外骨骼式上肢康复机器人辅助患者完成主动交互训练的功能.通过调节人机交互阻抗控制器的相应参数,机器人可以按照患者的运动意图完成不同的主动交互训练任务,并在运动出现偏差时予以矫正.控制器在设计实现过程中不要求复杂准确的动力学建模和参数识别,并有一定的抗干扰性和通用性.
    1)  本文责任编委 程龙
  • 图  1  五自由度外骨骼式上肢康复机器人

    Fig.  1  Five degree-of-freedom exoskeleton upper limb rehabilitation robot

    图  2  外骨骼机器人运动学分析

    Fig.  2  Kinetic analysis of robotic exoskeleton

    图  3  主动交互训练控制器结构框图

    Fig.  3  Control diagram of active interaction exercises

    图  4  无模型自适应滑模控制器结构图

    Fig.  4  Structure of model free adaptive sliding mode control

    图  5  交互力传感器分布图

    Fig.  5  Distribution of interaction force sensor

    图  6  交互力矩信号$\tau_{h1}$滤波效果比较

    Fig.  6  Filtering performance comparison of interaction torque $\tau_{h1}$

    图  7  虚拟现实引导界面

    Fig.  7  Guiding interface in virtual reality

    图  8  SimMechanics模型结构

    Fig.  8  Structure of SimMechanics model

    图  9  摩擦力矩与角速度的关系

    Fig.  9  The relationship between joint friction torques and angular velocity

    图  10  各关节交互力矩原始信号

    Fig.  10  The original signal of each joint interaction torque

    图  11  补偿力$F_{gy}$对主动训练末端轨迹的影响

    Fig.  11  The impact of compensation force $ F_ {gy} $ to the end trajectory of active training

    图  12  刚度系数$K_1$对主动训练末端轨迹的影响

    Fig.  12  The impact of stiffness coefficient $ K_1 $ to the end trajectory of active training

    图  13  五自由度主动交互训练仿真中主要变量的状态变化

    Fig.  13  Control results of five degree-of-freedom active interaction training exercise

    图  14  三自由度主动交互训练仿真中主要变量的状态变化

    Fig.  14  Control results of three degree-of-freedom active interaction training exercises

    表  1  外骨骼机器人运动学分析D-H参数表

    Table  1  D-H kinematics parameters of the robot

    i θi(°) di(m) ai(m) αi(°)
    0 90 0.0925 0.047 0
    1 q1 0.1425 0 -90
    - 0 -0.1240 0 0
    2 q2 0.0682 -0.277 0
    3 q3 0.0558 -0.220 0
    - 90 0 0 -90
    4 q4 0.079 0 90
    - 90 -0.0330 0 0
    5 q5 0.033 0 -90
    H 90 0.08 0 0
    下载: 导出CSV
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