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Journal of ZheJiang University (Engineering Science)  2024, Vol. 58 Issue (7): 1479-1487    DOI: 10.3785/j.issn.1008-973X.2024.07.017
    
Design of resistance neck rehabilitation robot system
Songlin HUANG1,2(),Xiujuan ZHENG1,2,Xiaoyue TAN1,2,Xing HU3,Haiyan TU1,2,*(),Kang LI4
1. College of Electrical Engineering, Sichuan University, Chengdu 610065, China
2. Key Laboratory of Information and Automation Technology of Sichuan Province, Sichuan University, Chengdu 610065, China
3. School of Mechanical Engineering, Sichuan University, Chengdu 610065, China
4. West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, China
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Abstract  

A new neck rehabilitation robot system was designed based on the theory of resistance rehabilitation training, as the existing rehabilitation devices with poor load accuracy and long load adjustment time. The newly designed mechanical structure converted the motor output tension into a neck resistance load, and the control system was divided into a control module and an algorithmic module to improve the system’s fluidity and stability. The magnetic field-oriented control algorithm was used to achieve precise control of the motor torque, providing a stable and controllable tension for neck rehabilitation training. Force sensors and posture gyroscopes were set up to prevent users form incorrect head and neck postures or unforeseen accidents. Assessment parameters of 10 subjects using the system for rehabilitation training were counted and the statistical results were analyzed. Simulation tests show that the magnetic field-oriented control algorithm improves the load accuracy and reduces the adjustment time. The consistency between the load testing results and the simulation results confirms the feasibility of the control scheme. The system performance tests indicated that the neck rehabilitation robot had a tension control error within 0.59 N and a response speed of 0.53 s, which meeting the standard for clinical use.



Key wordspermanent magnet synchronous motor      field oriented control      resistance training      sensor      rehabilitation robot     
Received: 31 October 2023      Published: 01 July 2024
CLC:  TP 242  
  R 496  
Fund:  国家自然科学基金重大研究计划集成项目(92248304).
Corresponding Authors: Haiyan TU     E-mail: hslhuangsonglin@126.com;haiyantu@163.com
Cite this article:

Songlin HUANG,Xiujuan ZHENG,Xiaoyue TAN,Xing HU,Haiyan TU,Kang LI. Design of resistance neck rehabilitation robot system. Journal of ZheJiang University (Engineering Science), 2024, 58(7): 1479-1487.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2024.07.017     OR     https://www.zjujournals.com/eng/Y2024/V58/I7/1479


抗阻式颈部康复机器人系统设计

已有颈部康复设备负载精度差且负载调节时间长,为此基于抗阻康复训练治疗理论设计新的颈部康复机器人系统. 设计机械结构,使电机输出的拉力转换成颈部的抗阻负载. 将控制系统分为控制模块与算法模块,提高系统的流畅性和稳定性. 利用磁场定向控制算法实现电机力矩的精准控制,为颈部康复训练提供稳定可控的拉力. 设置拉力传感器和姿态陀螺仪传感器,防止使用者头颈部姿态错误或未知事故. 统计10名受试者使用所设计系统进行颈部康复训练的评估参数,并对统计结果进行分析. 仿真测试显示,磁场定向控制算法能够提高负载精度并缩短调节时间;负载测试与仿真测试结果的一致性验证了控制方案的可行性. 系统性能测试表明,所设计的颈部康复机器人的拉力控制误差不超过0.59 N,响应速度为0.53 s,达到临床使用标准.


关键词: 永磁同步电机,  磁场定向控制,  抗阻训练,  传感器,  康复机器人 
Fig.1 Schematic diagram of cervical joint movement
Fig.2 Mechanical structure diagram of neck rehabilitation robot system
Fig.3 Overall framework of control system
Fig.4 Framework of double-loop field oriented control algorithm
Fig.5 Flow chart of control system
Fig.6 Schematic diagram of current loop simulation
Fig.7 Prototype of neck rehabilitation robot system
Fig.8 Simulated curve of torque current
Fig.9 Measured curve of torque current
Fig.10 Relationship between theoretical and measured tension
设备$ {e}_{{\mathrm{ss}}} $/NTu/s负载调节方式
文献[21]0.605.50电机转动调节
文献[25]0.505.00电机转动调节
文献[15]1.003.80电机转动调节
本研究0.590.53PI电流调节
Tab.1 Comparison results of performance and parameter of different neck rehabilitation equipment
Fig.11 Angular curves of lateral flexion movements under two tensions
ID性别侧屈旋转屈伸
$ {F}_{\mathrm{e}\mathrm{v}\mathrm{a}} $/N$ {\theta }_{\mathrm{m}\mathrm{a}\mathrm{x}} $/(°)$ {\theta }_{\mathrm{a}\mathrm{v}\mathrm{r}} $/(°)$ {\theta }_{\mathrm{s}\mathrm{t}\mathrm{d}} $/(°)$ {F}_{\mathrm{e}\mathrm{v}\mathrm{a}} $/N$ {\theta }_{\mathrm{m}\mathrm{a}\mathrm{x}} $/(°)$ {\theta }_{\mathrm{a}\mathrm{v}\mathrm{r}} $/(°)$ {\theta }_{\mathrm{s}\mathrm{t}\mathrm{d}} $/(°)$ {F}_{\mathrm{e}\mathrm{v}\mathrm{a}} $/N$ {\theta }_{\mathrm{m}\mathrm{a}\mathrm{x}} $/(°)$ {\theta }_{\mathrm{a}\mathrm{v}\mathrm{r}} $/(°)$ {\theta }_{\mathrm{s}\mathrm{t}\mathrm{d}} $/(°)
12636.0431.0023.0923373.9967.8082.2322225.0222.2321.749
22340.7833.5434.5032768.5365.7220.9573141.7638.4512.512
33528.6123.9143.0654262.5759.8452.6743337.5434.5252.879
41833.4230.6951.9091872.5370.5331.3412333.3532.4840.748
53026.3022.9621.4143766.5464.2571.8963243.5439.5884.547
62331.2028.5431.6272056.5452.3642.5492739.5437.0241.243
71536.0933.5182.3301878.8973.5962.0351540.2538.5141.245
82825.8222.7374.4002563.1560.8751.1842536.4734.1240.974
92229.5220.9685.1163074.5170.9742.8492034.8432.1451.545
101532.5330.5501.2172068.5461.6894.9481837.9834.5223.074
Tab.2 Statistics of ten test subjects under three types of training
Fig.12 Subject 1's angular curve under three types of training
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