The hybridisation of robot-assisted gait training and functional electrical stimulation (FES) can provide numerous physiological benefits to neurological patients. However, the design of an effective hybrid controller poses significant challenges. In this over-actuated system, it is extremely difficult to find the right balance between robotic assistance and FES that will provide personalised assistance, prevent muscle fatigue and encourage the patient's active participation in order to accelerate recovery. In this paper, we present an adaptive hybrid robot-FES controller to do this and enable the triadic collaboration between the patient, the robot and FES. A patient-driven controller is designed where the voluntary movement of the patient is prioritised and assistance is provided using FES and the robot in a hierarchical order depending on the patient's performance and their muscles' fitness. The performance of this hybrid adaptive controller is tested in simulation and on one healthy subject. Our results indicate an increase in tracking performance with lower overall assistance, and less muscle fatigue when the hybrid adaptive controller is used, compared to its non adaptive equivalent. This suggests that our hybrid adaptive controller may be able to adapt to the behaviour of the user to provide assistance as needed and prevent the early termination of physical therapy due to muscle fatigue.

翻译：机器人辅助步态训练与功能性电刺激（FES）的结合可为神经疾病患者带来诸多生理益处。然而，设计有效的混合控制器面临重大挑战。在这个过驱动系统中，寻找机器人辅助与FES之间的最佳平衡极为困难——既要提供个性化辅助，又要防止肌肉疲劳并鼓励患者主动参与以加速康复。本文提出一种自适应混合机器人-FES控制器以实现这一目标，并促成患者、机器人与FES之间的三元协作。我们设计了一种以患者为导向的控制器，优先考虑患者的自主运动，根据患者表现及其肌肉状态，按层级顺序依次采用FES和机器人提供辅助。通过仿真实验及一名健康受试者的测试验证了该混合自适应控制器的性能。结果表明，与非自适应等效控制器相比，采用混合自适应控制器时跟踪性能提升、总体辅助水平降低，且肌肉疲劳程度更轻。这表明该混合自适应控制器能够适应使用者行为，按需提供辅助，并预防因肌肉疲劳导致的物理治疗过早终止。