  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.

Robotics