While significant advancements have been made in the mechanical and task-specific controller designs of powered transfemoral prostheses, developing a task-adaptive control framework that generalizes across various locomotion modes and terrain conditions remains an open problem. This study proposes a task-adaptive learning quasi-stiffness control framework for powered prostheses that generalizes across tasks, including the torque-angle relationship reconstruction part and the quasi-stiffness controller design part. Quasi-stiffness is defined as the slope of the human joint's torque-angle relationship. To accurately obtain the torque-angle relationship in a new task, a Gaussian Process Regression (GPR) model is introduced to predict the target features of the human joint's angle and torque in the task. Then a Kernelized Movement Primitives (KMP) is employed to reconstruct the torque-angle relationship of a new task from multiple human demonstrations and estimated target features. Based on the torque-angle relationship of the new task, a quasi-stiffness control approach is designed for a powered prosthesis. Finally, the proposed framework is validated through practical examples, including varying speed and incline walking tasks. The proposed framework has the potential to expand to variable walking tasks in daily life for the transfemoral amputees.

翻译：尽管动力型大腿假肢在机械结构和特定任务控制器设计方面取得了显著进展，但开发一种能够泛化至多种运动模式及地形条件的任务自适应控制框架仍是一个未解难题。本研究提出了一种基于学习与任务自适应准刚度控制的动力假肢框架，该框架可泛化至不同任务，包括扭矩-角度关系重构模块与准刚度控制器设计模块。准刚度定义为人体关节扭矩-角度关系的斜率。为准确获取新任务中的扭矩-角度关系，引入高斯过程回归（GPR）模型预测该任务下人体关节角度与扭矩的目标特征。随后采用核化运动基元（KMP）方法，通过多个人类演示数据与估计的目标特征重构新任务的扭矩-角度关系。基于新任务的扭矩-角度关系，设计了一种针对动力假肢的准刚度控制方法。最后，通过变速度与变坡度行走任务的实际案例验证了该框架的有效性。该框架有望拓展至大腿截肢者日常生活中的多样化行走任务场景。