  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.
