一个离线的基于强化学习的自动调整框架,用于电动假肢中持续阻抗控制.
概括
强化学习 (RL) 提供了一种调整动力假肢控制步行的新方法. 这种方法优化了刚度参数,减少了手动调整,并改善了个性化的假肢功能.
科学领域:
- 生物医学工程 生物医学工程
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
背景情况:
- 动力假肢中的连续阻抗控制模仿了人类的关节行为.
- 目前用于步行控制的手动调是耗时的,并限制了适应性.
研究的目的:
- 开发一个强化学习 (RL) 驱动的框架,用于在动力假肢中连续阻抗控制的离线调整.
- 为了优化关键的刚性参数,在行走时增强膝盖动力学.
主要方法:
- 开发了一种基于高斯过程回归 (GPR) 的连续刚性运动学 (CSK) 模型来预测膝盖运动学.
- 采用深度决定性政策梯度 (DDPG) 算法用于离线优化刚度参数.
- 通过四名参与者的实验数据来训练和验证CSK模型.
主要成果:
- 基于度参数,CSK模型准确地预测了膝盖动力学.
- RL框架成功地优化了离线硬度参数,减少了手动调整.
- 证明了RL驱动的假肢控制调整方法的可行性和可靠性.
结论:
- RL驱动的离线调整是一种可行的方法,可以增强用于行走的动力假肢控制.
- 这种方法提高了个性化,并减少了参数调整的经验性质.
- 未来的工作包括在线人机循环集成,用于自适应实时控制.
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