UNS: Optimal Adaptive Control Methods for a Hybrid Exoskeleton

恢复行走和站立功能是最理想的干预措施之一，将提高截瘫患者的生活质量。有证据表明，与轮椅使用者相比，使用助行器的人经历的继发性医疗并发症更少。为了恢复行走和站立功能，将功能性电刺激（FES）与动力外骨骼相结合的混合外骨骼可能比基于FES的行走系统或单独的动力外骨骼更有利。对混合外骨骼控制方法的设计和评价研究很少。利用FES和电动马达，研究在肢体关节处产生共享力或协同力的自动控制方法。该算法将能够在FES引起的肌肉疲劳中保持FES和电动机之间的合作。共享控制方面的进步意味着可以设计出更小、更轻的外骨骼，因为FES可以额外利用用户固有的肌肉力量。在混合神经假体中使用FES也将提供治疗益处；例如，FES的应用可以改善心血管健康，运动技能再学习，增加肌肉质量和抗疲劳能力。因此，拟议的研究将通过改善行动障碍个人的生活质量和增加他们的社区参与，为社会提供实质性的利益。拟议的研究将研究基于强化学习（RL）原理的尖端控制方法，以同时控制FES和电动机。目前用于在混合装置中同时控制FES和主动矫形器的特殊技术不一定能适应肌肉疲劳-这是基于FES技术的主要限制因素。此外，FES和电动机的不同动力会导致行走时的不稳定，这可能导致因跌倒而受伤。新的RL控制技术将实时计算近似最优解，并将适用于由具有不同动力学的多个执行器驱动的动态系统。该提案的具体目的是研究和评估基于rl的混合腿部伸展机和混合步行装置的演员评论家控制方法。提出的实验将测量混合系统的总功率需求的任何减少及其对肌肉疲劳的依赖。该研究还将产生腓总神经（CPN）刺激的现象学模型，该模型用于在行走时引起髋关节屈曲。该模型将建立对CPN刺激的习惯化和激发特征的理解。与一名物理医师合作，实验将在身体健全的受试者和脊髓损伤的参与者中进行。

Restoration of walking and standing function is one of the most desired interventions that would improve quality of life of persons with paraplegia. Evidence supports that the users of walking devices experience fewer secondary medical complications than wheelchair users. To restore walking and standing function, a hybrid exoskeleton that combines functional electrical stimulation (FES) with a powered exoskeleton can be more advantageous than an FES-based walking system or a powered exoskeleton alone. Little research has gone into the design and evaluation of control methods for a hybrid exoskeleton. Using FES and an electric motor, the proposed research will investigate an automatic control method to produce shared or cooperative force at a limb joint. The proposed algorithm will be able to maintain cooperation between FES and the electric motor even when FES-induced muscle fatigue sets in. Advances in shared control imply that smaller and light weight exoskeletons can be designed because FES can be additionally used to exploit a user's inherent muscle power. The use of FES in the hybrid neuroprosthesis will also provide therapeutic benefits; e.g., application of FES improves cardiovascular fitness, motor-skill relearning, and increased muscle mass and fatigue resistance. Thus, the proposed study will provide substantial benefits to society by improving quality of life of individuals with mobility impairments and increasing their community participation.The proposed research will investigate cutting-edge control methods that are based on reinforcement learning (RL) principles for simultaneously controlling FES and an electric motor. The current ad hoc techniques used to control both FES and an active orthosis together in a hybrid device do not necessarily adapt with muscle fatigue - a major limiting factor in an FES-based technology. Moreover, the dissimilar dynamics of FES and the electric motor can cause instability during walking, which can potentially lead to injury due to falling. The new RL control techniques will compute approximate optimal solutions in real-time and will be applicable to dynamic systems that are driven by multiple actuators with dissimilar dynamics. The specific aims of the proposal are to investigate and evaluate RL-based actor-critic control methods on a hybrid leg extension machine and a hybrid walking device. The proposed experiments will measure any reduction in the overall power requirement of the hybrid system and its dependence on the muscle fatigue. The research will also result in a phenomenological model of common peroneal nerve (CPN) stimulation, which is used to elicit hip flexion during walking. This model will build an understanding on habituation and elicitation characteristics of the CPN stimulation. In collaboration with a physiatrist, experiments will be conducted on able-bodied subjects and a participant with spinal cord injury.