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Impedance-based control represents a prevalent strategy in the powered transfemoral prostheses because of its ability to reproduce natural walking.However, most existing studies have developed impedance-based prosthesiscontrollers for specific tasks, while creating a task-adaptive controller forvariable-task walking continues to be a significant challenge. This articleproposes a task-adaptive quasi-stiffness control framework for poweredprostheses that generalizes across various walking tasks, including thetorque-angle relationship reconstruction part and the quasi-stiffnesscontroller design part. A Gaussian Process Regression model is introduced topredict the target features of the human joints angle and torque in a new task.Subsequently, a Kernel Movement Primitives is employed to reconstruct thetorque-angle relationship of the new task from multiple human referencetrajectories and estimated target features. Based on the torque-anglerelationship of the new task, a quasi-stiffness control approach is designedfor a powered prosthesis. Finally, the proposed framework is validated throughpractical examples, including varying speeds and inclines walking tasks.Notably, the proposed framework not only aligns with but frequently surpassesthe performance of a benchmark finite state machine impedance controllerwithout necessitating manual impedance tuning and has the potential to expandto variable walking tasks in daily life for the trans-femoral amputees.

A Learning Quasi-stiffness Control Framework of a Powered Trans-femoral Prosthesis for Adaptive Speed and Incline Walking