同步学习增强了基于DNN的混合外骨架的DNN和入口控制
概括
这项研究介绍了混合外部骨的新型控制器,将功能电刺激 (FES) 和机器人电机结合起来用于神经康复. 这些先进的系统增强了康复机器人的患者特定控制和安全.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 神经康复疗法 神经康复疗法
- 控制系统工程 控制系统工程
背景情况:
- 神经系统疾病 (NCs) 损害了运动功能,需要先进的康复策略.
- 融合功能电刺激 (FES) 和机器人辅助的混合外骨架提供了有前途的康复解决方案.
- 有效控制这些混合系统需要管理人类肌肉激活和机器人运动动力学.
研究的目的:
- 开发和评估混合外骨的新型控制策略.
- 提高基于FES的康复设备的性能和安全性.
- 为了实现个性化和适应性控制的个人与神经疾病.
主要方法:
- 开发两个控制器:一个基于深度神经网络 (DNN) 的控制器和一个基于Admittance的控制器.
- DNN用于近似不确定的混合外部骨动态,以有效控制FES,并提供多次时间尺度重量更新 (线下和在线).
- 基于接入的控制器利用扭矩反进行自适应式电机控制,优先考虑参与者的安全和舒适.
主要成果:
- 基于DNN的控制器通过一次并发学习 (CL) 启发的术语证明了更好的学习性能.
- 基于Admittance的控制器允许非预先确定的轨迹,增强参与者交互和安全.
- 基于Lyapunov的稳定性分析证实了这两种控制系统的稳定性和安全性.
结论:
- 提出的基于DNN和基于Admittance的控制器对于神经康复中的混合外骨系统是有效的.
- 这些先进的控制策略提高了系统性能,适应性和用户安全.
- 这项研究有助于推进用于个性化物理治疗的智能机器人系统的发展.
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