Dunkelberger Nathan, Berning Jeffrey, Schearer Eric M, O'Malley Marcia K
Department of Mechanical Engineering, Mechatronics and Haptics Interfaces Laboratory, Rice University, Houston, TX, United States.
Center for Human Machine Systems, Department of Mechanical Engineering, Cleveland State University, Cleveland, OH, United States.
Front Neurorobot. 2023 Apr 6;17:1127783. doi: 10.3389/fnbot.2023.1127783. eCollection 2023.
Individuals who have suffered a cervical spinal cord injury prioritize the recovery of upper limb function for completing activities of daily living. Hybrid FES-exoskeleton systems have the potential to assist this population by providing a portable, powered, and wearable device; however, realization of this combination of technologies has been challenging. In particular, it has been difficult to show generalizability across motions, and to define optimal distribution of actuation, given the complex nature of the combined dynamic system.
In this paper, we present a hybrid controller using a model predictive control (MPC) formulation that combines the actuation of both an exoskeleton and an FES system. The MPC cost function is designed to distribute actuation on a single degree of freedom to favor FES control effort, reducing exoskeleton power consumption, while ensuring smooth movements along different trajectories. Our controller was tested with nine able-bodied participants using FES surface stimulation paired with an upper limb powered exoskeleton. The hybrid controller was compared to an exoskeleton alone controller, and we measured trajectory error and torque while moving the participant through two elbow flexion/extension trajectories, and separately through two wrist flexion/extension trajectories.
The MPC-based hybrid controller showed a reduction in sum of squared torques by an average of 48.7 and 57.9% on the elbow flexion/extension and wrist flexion/extension joints respectively, with only small differences in tracking accuracy compared to the exoskeleton alone.
To realize practical implementation of hybrid FES-exoskeleton systems, the control strategy requires translation to multi-DOF movements, achieving more consistent improvement across participants, and balancing control to more fully leverage the muscles' capabilities.
遭受颈脊髓损伤的个体将恢复上肢功能以完成日常生活活动视为优先事项。混合功能性电刺激-外骨骼系统有潜力通过提供一种便携、动力驱动且可穿戴的设备来辅助这一人群；然而，实现这种技术组合一直具有挑战性。特别是，鉴于组合动态系统的复杂性，很难证明其在各种动作中的通用性，也难以定义最佳的驱动分布。
在本文中，我们提出了一种使用模型预测控制（MPC）公式的混合控制器，该控制器结合了外骨骼和功能性电刺激系统的驱动。MPC成本函数旨在将单自由度上的驱动分布为有利于功能性电刺激控制努力，降低外骨骼功耗，同时确保沿不同轨迹的平稳运动。我们的控制器在九名身体健全的参与者身上进行了测试，使用功能性电刺激表面刺激与上肢动力外骨骼配对。将混合控制器与单独的外骨骼控制器进行比较，我们在使参与者通过两条肘部屈伸轨迹以及分别通过两条腕部屈伸轨迹移动时测量了轨迹误差和扭矩。
基于MPC的混合控制器在肘部屈伸和腕部屈伸关节上的平方扭矩总和分别平均降低了48.7%和57.9%，与单独的外骨骼相比，跟踪精度只有微小差异。
为了实现混合功能性电刺激-外骨骼系统的实际应用，控制策略需要转化为多自由度运动，在参与者中实现更一致的改善，并平衡控制以更充分地利用肌肉能力。
