基于功能性电刺激的肘部运动迭代学习控制研究
Study on Iterative Learning Control of Elbow Movement Based on Functional Electrical Stimulation
操瑞钰1
作者信息
- 1. 中国科学院 沈阳自动化研究所 机器人学国家重点实验室,沈阳 110016;中国科学院 机器人与智能制造创新研究院,沈阳 110169;中国科学院大学 计算机科学与技术学院,北京 100049
- 折叠
摘要
功能性电刺激(functional electrical stimulation,FES)是治疗脑卒中后运动障碍的重要方法.考虑到现有动力学建模方法复杂、控制精度有待提高,该文提出了一种基于FES的肘部运动的带有遗忘因子的迭代学习控制方法.该研究利用Hammerstein结构建立肘部肌肉骨骼模型,在此基础上设计了带有遗忘因子的迭代学习控制的FES系统.通过计算样本实际输出与模型输出角度之间的均方根误差和最大角度误差,检验所建立的肌骨模型的可靠性.通过仿真分析和实际控制实验,评估了闭环FES系统的控制效果,并与传统的迭代学习控制FES系统进行了对比,说明了控制方法的有效性.研究结果表明,所建立的肘部肌骨模型适用于研究电刺激下肘关节的运动特性,并且带有遗忘因子的迭代学习控制方法在肘部控制中表现出更优越的性能.
Abstract
Functional electrical stimulation(FES)is an important method for treating post-stroke movement disorders.Considering that the existing kinetic modeling methods are complicated and the control accuracy needs to be im-proved,this paper proposes an iterative learning control method with a forgetting factor for elbow movement based on FES.This research utilizes the Hammerstein structure to establish the musculoskeletal model of the elbow,on the ba-sis of which the FES system with iterative learning control with forgetting factor is designed.The reliability of the established musculoskeletal model is examined by calculating the root-mean-square error and the maximum angular error between the actual output of the sample and the output angle of the model.The control effect of the closed-loop FES system is evaluated through simulation analysis and actual control experiments,and compared with the tradi-tional iterative learning control FES system to illustrate the effectiveness of the control method.The results show that the established musculoskeletal model of the elbow is suitable for studying the kinematic characteristics of the elbow joint under electrical stimulation,and that the iterative learning control method with a forgetting factor exhibits supe-rior performance in elbow control.
关键词
功能性电刺激/肌骨模型/迭代学习控制/遗忘因子Key words
functional electrical stimulation(FES)/musculoskeletal model/iterative learning control(ILC)/forgetting factor引用本文复制引用
基金项目
国家自然科学基金项目(62273336)
国家自然科学基金项目(92048302)
国家自然科学基金项目(U20A20197)
国家重点研发计划项目(2022YFF1202500)
国家重点研发计划项目(2022YFF1202502)
出版年
2024
NETL
NSTL
万方数据