论文标题

部分可观测时空混沌系统的无模型预测

Emulating Human Kinematic Behavior on Lower-Limb Prostheses via Multi-Contact Models and Force-Based Nonlinear Control

论文作者

Gehlhar, Rachel, Ames, Aaron D.

论文摘要

脚踝推断在很大程度上有助于人类步行的肢体能量产生,从而使运动更加顺畅,更有效。向截肢者提供这项净积极工作需要积极的假体,但有可能实现更自然的辅助运动。为此,本文将运动的多连接模型与基于力的非线性优化控制器一起使用,以实现2个受试者的动力透明假体上的类似人类的运动学行为,包括脚踝推断。特别是,我们利用基于模型的控制方法进行动态的双足机器人步行,以开发一种系统的方法,以实现不需要特定于主体的调整的动力假体上的人类行走。我们首先综合一个优化问题,该问题产生类似于人类关节轨迹的步态,并通过基于控制Lyapunov函数的基于Lyapunov函数的非线性控制器实现这些步态,从而对实时地面压力反应力和与人类的相互作用作用做出反应。所提出的控制器是针对两个受试者的假体实施的,而无需在受试者之间进行调整,从而模拟了假体关节的特定主体人类运动学趋势。这些实验结果表明,与传统方法相比,我们基于力的非线性控制方法可以更好地跟踪人类运动轨迹。

Ankle push-off largely contributes to limb energy generation in human walking, leading to smoother and more efficient locomotion. Providing this net positive work to an amputee requires an active prosthesis, but has the potential to enable more natural assisted locomotion. To this end, this paper uses multi-contact models of locomotion together with force-based nonlinear optimization-based controllers to achieve human-like kinematic behavior, including ankle push-off, on a powered transfemoral prosthesis for 2 subjects. In particular, we leverage model-based control approaches for dynamic bipedal robotic walking to develop a systematic method to realize human-like walking on a powered prosthesis that does not require subject-specific tuning. We begin by synthesizing an optimization problem that yields gaits that resemble human joint trajectories at a kinematic level, and realize these gaits on a prosthesis through a control Lyapunov function based nonlinear controller that responds to real-time ground reaction forces and interaction forces with the human. The proposed controller is implemented on a prosthesis for two subjects without tuning between subjects, emulating subject-specific human kinematic trends on the prosthesis joints. These experimental results demonstrate that our force-based nonlinear control approach achieves better tracking of human kinematic trajectories than traditional methods.

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