Learning Task-adaptive Quasi-stiffness Control for A Powered Transfemoral Prosthesis

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.