Coordinating Electrical Stimulation and Motor Assist in a Hybrid Neuroprosthesis Using Control Strategies Inspired by Human Motor Control

功能性电刺激（FES）和动力外骨骼是两种用于恢复截瘫患者行走的技术。FES包括用于激活腿部肌肉的低水平电流。相比之下，动力外骨骼使用安装在外部可穿戴框架上的电动机来移动下肢关节。这些技术都有局限性。在这个项目中，它们被协同使用，以创建一个混合神经假体，解决这些个别的缺点。混合方法成功的关键是协调控制多个FES激活的肌肉和电动机。这种协调控制必须随着时间的推移而适应，因为FES引起的疲劳降低了用户肌肉跟随所需步行运动的能力。该项目产生的控制算法将使一致的步行运动，尽管FES引起的肌肉疲劳，有助于出现一个适应性强，重量轻的混合外骨骼与FES系统或单独的动力外骨骼有很大的优势。这项研究将加强身体活动，改善下肢功能受损者的活动能力，使他们能够更多地参与社区活动，提高生活质量。在混合外骨骼中，由于冗余，协调多个FES激活的肌肉和电动马达可能是复杂的。此外，更新多个控制输入以考虑系统不确定性和FES引起的肌肉疲劳对于实时控制来说在计算上是昂贵的。本研究将为混合式神经义肢设计适应性低维控制方法。受人类运动控制启发的控制结构可以用于使用较少数量的命令来控制多个效应器系统。因此，将减少与混合神经假体的闭环控制相关的冗余和复杂性。动态优化将用于设计低维行走控制模块。然后利用基于李雅普诺夫的稳定性分析方法，设计控制结构的更新和反馈控制律。这将确保稳定性和跟踪，尽管系统的不确定性和肌肉疲劳。最后，新的控制结构将在健全的主体上进行实验验证。

Functional electrical stimulation (FES) and powered exoskeletons are two technologies being used to restore walking in individuals with paraplegia. FES comprises low-level electrical currents applied to activate leg muscles. In contrast, powered exoskeletons use electric motors mounted on an external wearable frame to move lower-limb joints. Each of these technologies has limitations. In this project they are used in synergy, to create a hybrid neuroprosthesis that addresses these individual drawbacks. Critical to the success of the hybrid approach is coordinated control of multiple FES-activated muscles and the electric motors. This coordinated control must adapt over time as FES-induced fatigue degrades the ability of the user's muscles to follow the desired walking motion. The control algorithms resulting from this project will enable consistent walking movements despite FES-induced muscle fatigue, contributing to the emergence of an adaptable and lightweight hybrid exoskeleton with substantial advantages over FES systems or powered exoskeletons alone. This research will enhance physical activity and improve mobility for individuals with impaired lower limb function, enabling greater community participation and increased quality of life. In a hybrid exoskeleton, coordinating multiple FES-activated muscles and electric motors can be complicated due to redundancy. Further, updating multiple control inputs to account for system uncertainty and FES-induced muscle fatigue can be computationally expensive for the real-time control. This research will design adaptive low-dimensional control methods for the hybrid neuroprosthesis. The human motor control inspired control structure can be used to control multiple effector system using a fewer number of commands. Thus, redundancy and complexity associated with the closed-loop control of the hybrid neuroprosthesis will be reduced. Dynamic optimization will be used to design low-dimensional control modules for walking. Then by using Lyapunov-based stability analysis, update and feedback control laws for the control structure will be designed. This will ensure stability and tracking despite system uncertainty and muscle fatigue. Finally, the new control structure will be experimentally verified on able-bodied subjects.