Impedance-based control represents a prevalent strategy in the development of powered transfemoral prostheses. However, creating a task-adaptive, tuning-free controller that effectively generalizes across diverse locomotion modes and terrain conditions continues to be a significant challenge. This letter proposes a tuning-free and task-adaptive quasi-stiffness control framework for powered prostheses that generalizes across various walking tasks, including the torque-angle relationship reconstruction part and the quasi-stiffness controller design part. A Gaussian Process Regression (GPR) model is introduced to predict the target features of the human joint angle and torque in a new task. Subsequently, a Kernelized Movement Primitives (KMP) is employed to reconstruct the torque-angle relationship of the new task from multiple human reference trajectories 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 speeds and inclines walking tasks. Notably, the proposed framework not only aligns with but frequently surpasses the performance of a benchmark finite state machine impedance controller (FSMIC) without necessitating manual impedance tuning and has the potential to expand to variable walking tasks in daily life for the transfemoral amputees.

翻译：基于阻抗的控制方法是动力型股骨假体开发中的主流策略。然而，构建一种能有效泛化至多种运动模式与地形条件、兼具任务自适应性与免调参特性的控制器，仍是重大挑战。本文提出一种面向动力型假体的免调参任务自适应准刚度控制框架，该框架可泛化至多种行走任务，包含力矩-角度关系重构模块与准刚度控制器设计模块两部分。引入高斯过程回归（GPR）模型预测新任务中人体关节角度与力矩的目标特征，进而采用核化运动基元（KMP）从多个人体参考轨迹与估计的目标特征中重构新任务的力矩-角度关系。基于新任务的力矩-角度关系，设计适用于动力型假体的准刚度控制方法。最后，通过变速度与变坡度行走任务等实际案例验证所提框架。值得注意的是，该框架不仅与基准有限状态机阻抗控制器（FSMIC）性能相当，更常在其之上无需手动阻抗调参，并具备拓展至股骨截肢者日常变参行走任务的潜力。