[1]高建设,刘陆骐,王 杰,等.基于模糊控制的上肢康复机器人变导纳控制[J].郑州大学学报(工学版),2024,(01):12-20.[doi:10.13705/j.issn.1671-6833.2024.01.002]
 GAO Jianshe,LIU Luqi,WANG Jie,et al.Variable Admittance Control of Upper Limb Rehabilitation Robot Based on Fuzzy Control[J].Journal of Zhengzhou University (Engineering Science),2024,(01):12-20.[doi:10.13705/j.issn.1671-6833.2024.01.002]
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基于模糊控制的上肢康复机器人变导纳控制()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
期数:
2024年01期
页码:
12-20
栏目:
出版日期:
2024-01-19

文章信息/Info

Title:
Variable Admittance Control of Upper Limb Rehabilitation Robot Based on Fuzzy Control
作者:
高建设 刘陆骐 王 杰 李雪晓 丁顺良 高亦阳 王 轩
1. 郑州大学 机械与动力工程学院,河南 郑州 450001;2. 安阳鑫盛机床股份有限公司, 河南 安阳 455000
Author(s):
GAO Jianshe LIU Luqi WANG Jie LI Xuexiao DING Shunliang GAO Yiyang WANG Xuan
1. School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China;2. Anyang Xinsheng Machine Tool Co. , Ltd. , Anyang 455000, China
关键词:
导纳控制 模糊控制 人机交互力 上肢康复机器人 康复训练
Keywords:
admittance control fuzzy control human-computer interaction force upper limb rehabilitation robot rehabilitation training
DOI:
10.13705/j.issn.1671-6833.2024.01.002
文献标志码:
A
摘要:
传统固定参数的导纳模型无法对上肢康复机器人的柔顺性实时调节,而现有变参数导纳模型需要结合实 际康复需求进行改善。 为此,以自主研发的串并混联的末端牵引式上肢康复机器人为研究对象,结合模糊控制与 导纳控制,提出了一种面向实际康复需求的新型变导纳控制策略,制定了 4 条有利于康复效率与安全的模糊规则。 该策略利用交互力误差及其变化率作为模糊控制输入,实时改变导纳模型的参数,实现柔顺性自主调节。 仿真和 实验结果验证了所提出的变导纳模型控制策略可行性和所制定的 4 条模糊规则的有效性。 在患者可适应训练强 度的场景中,相较于固定导纳模型,变导纳模型追踪给定路径时产生的冗余路径最多可降低 56. 13%,提高了康复 训练效率;当康复运动超出患者可承受范围时,变导纳模型可提前 0. 5 s 改变追踪路径,提高了康复的安全性。
Abstract:
Traditional fixed-parameter admittance models could not adjust the compliance of upper limb rehabilitation robots in real-time, existing variable-parameter admittance models required improvement based on actual rehabilitation needs. To address this issue, a novel variable-admittance control strategy was proposed for upper limb rehabilitation robots with a self-developed series-parallel hybrid end-effector traction structure, combining fuzzy and admittance control and tailored to actual rehabilitation needs. Four fuzzy rules that could benefit rehabilitation efficiency and safety were developed. This strategy proposed the use of interaction force error and its rate of change as inputs to fuzzy control, to adjust admittance model parameters and achieve autonomous compliance control in real time. Simulation and experimental results validated the feasibility of the proposed variable-admittance control strategy and the effectiveness of the developed four fuzzy rules. In scenarios where patients could adapt to training intensity, the variable-admittance model could reduce the redundant path generated during path tracking by up to 56. 13% thus improving rehabilitation training efficiency. When rehabilitation movements exceeded the patients′ tolerance limit, the variable-admittance model could change the tracking path half a second earlier, improving rehabilitation safety

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更新日期/Last Update: 2024-01-23