对基于传感的运动意图估计和安全跟踪的不同控制器的评估在模拟的LSTM基于网络的肘部外骨架机器人中
Farshad Shakeriaski1, Masoud Mohammadian1
1Faculty of Science and Technology, University of Canberra, Canberra, ACT 2617, Australia.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究开发了一种用于肘部外骨架的智能控制框架,使用高密度表面电肌图 (HD-sEMG) 和LSTM网络改进了实时运动意图跟踪. 滑动模式控制器在神经康复应用中表现出卓越的准确性和稳定性.
科学领域:
- 生物医学工程 生物医学工程
- 康复机器人 康复机器人
- 神经科学是一个神经科学.
背景情况:
- 对于康复的肘部外骨控制受到实时意图估计挑战的阻碍.
- 每年有数百万患者需要积极康复,因为他们患有中风和脊髓损伤等疾病.
- 外骨架的精确运动跟踪可以恢复独立性并降低医疗保健成本.
研究的目的:
- 为肘部外骨架开发智能控制框架,实现精确和安全的实时运动意图跟踪.
- 从高密度表面肌电图 (HD-sEMG) 信号中估计用户的运动意图.
- 为了比较PID,阻抗和滑动模式控制器用于外骨控制的性能.
主要方法:
- 利用了12名健康个体的公共HD-sEMG数据集,跨越四个同位素任务和三个努力水平.
- 预处理EMG信号并提取了13个时间域特征.
- 训练LSTM网络以实时估计所需的关节角度,然后进行控制器实现 (PID,阻抗,滑动模式).
主要成果:
- LSTM模型实现了高精度 (RMSE=0.630 Nm,R2=0.965,Pearson=0.985),比传统方法提高了47%的R2.
- 滑动模式控制器表现出卓越的性能,跟踪错误最小 (平均: RMSE=0.21 Nm,R2≈0.96) 在所有任务和努力水平上.
- 阻抗控制器在曲/延伸方面表现良好,但随着前置/上置而恶化;PID控制不合适.
结论:
- 拟议的LSTM-滑动模式混合架构为实时意图监控提供了卓越的准确性,稳定性和透明度.
- 这一框架显示了高级上肢外骨在神经康复和辅助应用中的重大前景.
- 为实时临床实施,建议进行进一步的硬件验证.
关键词:
在PID控制中,PID控制器深度学习是一种深度学习.阻抗控制控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗控制阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗阻抗滑动模式控制器的滑动模式控制器上肢辅助外骨架 机器人 肘部 整形相关概念视频
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